Large language model interaction processing method and device

Abstract

The embodiment of the invention provides a big language model interaction processing method and device.The big language model interaction processing method comprises the steps that in the big language model interaction processing process, an access record, uploaded by a user terminal, of user interaction content of a big language model is obtained, the method comprises the steps of performing access popularity calculation according to an access record to obtain an access popularity index of user interaction content, further performing correction processing on the access popularity index based on an interaction correction parameter to obtain a corrected popularity index, and finally generating an access popularity control of the user interaction content according to the corrected popularity index and returning the access popularity control to a user terminal. Therefore, rapid access interaction is realized for the user interaction content of the large language model.

Description

Technical Field

[0001] This document relates to the field of large model technology, and in particular to a method and apparatus for interactive processing of large language models. Background Technology

[0002] With the continuous development and promotion of the Internet and artificial intelligence, many services in user interaction scenarios with large language models can be automated with the help of large language models. Specifically, in the dialogue scenario where users access services, large language models can generate targeted answers based on the user's input questions. However, as more and more services use large language models, the competition between services becomes more and more intense. In this case, higher requirements are placed on the service processing through large language models. Summary of the Invention

[0003] This specification provides one or more embodiments of a large language model interaction processing method, the method comprising: acquiring access records of user interaction content of a large language model uploaded by a user terminal; calculating access popularity based on the access records to obtain an access popularity index for the user interaction content; correcting the access popularity index based on interaction correction parameters to obtain a corrected popularity index; generating an access popularity control for the user interaction content based on the corrected popularity index and returning it to the user terminal for access interaction based on the access popularity control.

[0004] This specification provides one or more embodiments of another large language model interaction processing method, the method comprising: obtaining a trigger command from a user regarding user interaction content on a large language model; responding to the trigger command, obtaining an access popularity control for the user interaction content from a server; the access popularity control is generated based on a corrected popularity index obtained by correcting the access popularity index of the user interaction content, the access popularity index being calculated based on the access records of the user interaction content; and performing display processing of the access popularity control to switch the interaction content according to user operations.

[0005] This specification provides one or more embodiments of a large language model interactive processing device, comprising: a record acquisition module configured to acquire access records of user interaction content of a large language model uploaded by a user terminal; an index calculation module configured to calculate access popularity based on the access records to obtain an access popularity index of the user interaction content; an index correction module configured to correct the access popularity index based on interaction correction parameters to obtain a corrected popularity index; and a control return module configured to generate an access popularity control of the user interaction content based on the corrected popularity index and return it to the user terminal for access interaction based on the access popularity control.

[0006] This specification provides one or more embodiments of another large language model interactive processing device, including: an instruction acquisition module configured to acquire a trigger instruction from a user regarding user interaction content of a large language model; a control acquisition module configured to, in response to the trigger instruction, acquire an access popularity control of the user interaction content from a server; the access popularity control is generated based on a corrected popularity index obtained by correcting the access popularity index of the user interaction content, the access popularity index being calculated based on the access records of the user interaction content; and a control display module configured to display the access popularity control to switch the interactive content according to user operations.

[0007] This specification provides one or more embodiments of a large language model interactive processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: acquire access records of user interaction content of a large language model uploaded by a user terminal; calculate access popularity based on the access records to obtain an access popularity index for the user interaction content; correct the access popularity index based on interaction correction parameters to obtain a corrected popularity index; generate an access popularity control for the user interaction content based on the corrected popularity index and return it to the user terminal for access interaction based on the access popularity control.

[0008] This specification provides one or more embodiments of another large language model interactive processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: obtain a trigger instruction from a user regarding user interaction content on a large language model; in response to the trigger instruction, obtain an access popularity control for the user interaction content from a server; the access popularity control is generated based on a corrected popularity index obtained by correcting the access popularity index of the user interaction content, the popularity index being calculated based on access records of the user interaction content; and perform display processing of the access popularity control to switch interactive content according to user operations.

[0009] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions. When executed, these computer-executable instructions implement the following process: acquiring access records of user interaction content of a large language model uploaded by a user terminal; calculating access popularity based on the access records to obtain an access popularity index for the user interaction content; correcting the access popularity index based on interaction correction parameters to obtain a corrected popularity index; generating an access popularity control for the user interaction content based on the corrected popularity index and returning it to the user terminal for access interaction based on the access popularity control.

[0010] This specification provides one or more embodiments of another computer-readable storage medium for storing computer-executable instructions, which, when executed, perform the following process: Obtaining a trigger instruction from a user regarding user interaction content for a large language model; Responding to the trigger instruction, obtaining an access popularity control for the user interaction content from a server; The access popularity control is generated based on a corrected popularity index obtained by correcting the access popularity index of the user interaction content, the popularity index being calculated based on the access records of the user interaction content; Performing display processing of the access popularity control to switch the interactive content according to user operations. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 A schematic diagram illustrating the implementation environment of a large language model interactive processing method provided in one or more embodiments of this specification; Figure 2 A flowchart illustrating a large language model interaction processing method provided in one or more embodiments of this specification; Figure 3 A schematic diagram of a first type of user interaction page provided for one or more embodiments of this specification; Figure 4A schematic diagram of a second type of user interaction page provided for one or more embodiments of this specification; Figure 5 A schematic diagram of a third user interaction page provided for one or more embodiments of this specification; Figure 6 A schematic diagram of a fourth user interaction page provided for one or more embodiments of this specification; Figure 7 A schematic diagram of a fifth user interaction page provided for one or more embodiments of this specification; Figure 8 A timing diagram of a large language model interaction processing method for user interaction content access scenarios provided in one or more embodiments of this specification; Figure 9 A flowchart of another large language model interaction processing method provided in one or more embodiments of this specification; Figure 10 A schematic diagram of an embodiment of a large language model interactive processing device provided in one or more embodiments of this specification; Figure 11 A schematic diagram of another embodiment of a large language model interactive processing device provided in one or more embodiments of this specification; Figure 12 A schematic diagram of the structure of a large language model interactive processing device provided for one or more embodiments of this specification; Figure 13 This is a schematic diagram of the structure of another large language model interactive processing device provided in one or more embodiments of this specification. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0013] The large language model interaction processing method provided in one or more embodiments of this specification is applicable to the implementation environment of user interaction content access. (Refer to...) Figure 1 The implementation environment includes at least: Server 101 and user terminal 102; Among them, server 101 is equipped with a large language model 103. Server 101 is used to calculate and correct the access popularity based on the user's access records of user interaction content on the large language model, and to generate access popularity control of user interaction content. Server 101 can be a single server, a server cluster consisting of several servers, or one or more cloud servers in a cloud computing platform.

[0014] User terminal 102 performs corresponding processing by accessing the interaction interface of large language model 103. In addition, when user terminal 102 obtains the user's trigger instruction for user interaction content of large language model 103, it obtains the access popularity control of user interaction content from server 102 and displays it. User terminal 102 can be a mobile phone, personal computer, tablet computer, e-book reader, device for information interaction based on VR (Virtual Reality) and AR (Augmented Reality), vehicle terminal, IoT device, wearable smart device, laptop computer and desktop computer, etc.

[0015] In this implementation environment, during the interactive processing of the large language model 103, the user terminal 102 pre-uploads the access records of the user interaction content of the large language model 103 to the server 102. The server 102 calculates the access popularity index of the user interaction content based on the access records, and corrects the access popularity index based on the interaction correction parameters to obtain the corrected popularity index. Furthermore, it generates the access popularity control of the user interaction content based on the corrected popularity index and returns it to the user terminal 102, enabling the user terminal 102 to perform access interaction based on the access popularity control, thereby realizing a quick access interaction for the user interaction content of the large language model 103.

[0016] This specification provides one or more embodiments of a large language model interactive processing method as follows: Reference Figure 2 The large language model interaction processing method provided in this embodiment can be applied to a server. The method specifically includes steps S202 to S208.

[0017] Step S202: Obtain access records of user interaction content of the large language model uploaded by the user terminal.

[0018] In this embodiment, the large language model refers to a large language model that is based on the base large language model, finely tuned, and integrates tool calling capabilities to provide users with natural language dialogue interaction and contextualized services. The large language model can be a multimodal model (multimodal large language model), or a single-modal large language model. The large language model can also be a large language model deployed by an intelligent agent. In addition, the large language model can be replaced by an intelligent agent, and correspondingly, the user interaction content of the large language model can also be replaced by the user interaction content of the intelligent agent. Furthermore, the large language model and its corresponding content involved in the following can all be replaced by the intelligent agent and its corresponding content.

[0019] User interaction content of a large language model refers to the interaction results generated by the large language model during user interaction. Specifically, it is the output data obtained by the large language model after processing the user-submitted input data. User interaction with the large language model can be dialogic, text-based, or non-textual, such as inputting images, code, or files into the large language model for dialogic interaction. In addition, user interaction with the large language model can be dialogic or tool access interaction, such as triggering various tools provided by the large language model, such as document generation tools, audio and video processing tools, etc., to interact with the large language model.

[0020] Specifically, user interaction content can be the dialogue interaction content generated by at least one dialogue interaction between the user and the large language model. The dialogue interaction content of each dialogue interaction includes the dialogue output results generated by the large language model, and may also include user input data, that is, the dialogue interaction content may include dialogue questions or dialogue input entered by the user; similarly, user interaction content can also be the access interaction content generated by at least one tool access interaction between the user and the large language model, and may also include user input data entered by the user for the tool deployed by the large language model; or, user interaction content can also be the dialogue interaction content of the user and the large language model in dialogue lists, dialog boxes, or dialogue pages.

[0021] User interaction content access records refer to data used to record specific access actions or operations during the process of users accessing and viewing user interaction content. In the specific execution process, access records can be collected through the tracking code deployed in the interaction interface of the large language model.

[0022] Optionally, the access records include at least one of the following: access time records, evaluation records, dialogue interaction records, and operation records.

[0023] Among them, the access time record is used to record the time when a user accesses user interaction content, such as the duration of the user's access to user interaction content and / or the duration of selected reading; The evaluation record is used to record users' evaluation information on user interaction content, such as the number of positive evaluations (number of likes) of users on the answers to questions generated by the large language model, and / or the number of negative evaluations of users on the answers to questions; Dialogue interaction records are used to record dialogue-related information about user interactions, such as the number of times a question answer was regenerated, and / or the number of times a follow-up question was asked about the answer. Operation logs are used to record user actions related to user interaction content, such as the number or number of times a user deletes a question answer, the number or number of times a user favorites a question answer, and / or the number or number of times a user saves a summary of a question answer.

[0024] Step S204: Calculate the access popularity based on the access records to obtain the access popularity index of the user interaction content.

[0025] In practice, based on the access records of user interaction content in the large language model, that is, based on the access records of user interaction content, the access popularity is calculated from the access records of user interaction content to obtain the access popularity index of user interaction content. The access popularity index is an indicator used to characterize the access popularity of user interaction content.

[0026] In the specific implementation process, considering that the access records of users accessing user interaction content may contain access records of different data dimensions, the access records can be standardized, and the obtained standardized access records can be input into the access popularity algorithm to calculate the access popularity index of user interaction content.

[0027] In one optional implementation of this embodiment, the access popularity is calculated based on access records to obtain an access popularity index for user interaction content, including: The access record items contained in the access record are standardized to obtain each standard record item; Each standard record item is input into the access popularity algorithm to calculate the access popularity index.

[0028] The access record item refers to each access record contained in the access record. The access record item can be collected through the instrumentation code deployed through the interaction interface of the large language model. Optionally, the access record item includes at least one of the following: access time record, evaluation record, dialogue interaction record, and operation record.

[0029] The access popularity algorithm is a weighted algorithm that calculates the access popularity by weighting each standard record item with its corresponding weight. For example, the access popularity algorithm is: HeatValue = α×T + β×L + γ×F; where HeatValue is the access popularity index, T, L, and F are access time records, evaluation records, and dialogue interaction records, respectively, and α, β, and γ are the weights of the access time records, evaluation records, and dialogue interaction records, respectively.

[0030] In practical applications, the impact of access records from different data dimensions on access popularity may vary. To improve the accuracy of access popularity calculation, corresponding access popularity weights can be set for access records from different data dimensions. Furthermore, to make access popularity calculation more consistent with the actual interaction scenarios of large language models, corresponding access popularity weights can be set for the same data dimension in different interaction scenarios. For example, one access popularity weight can be set for dialogue interaction scenarios, and another access popularity weight can be set for tool access interaction scenarios. In this case, the corresponding access popularity weight of the access record is determined according to the interaction scenario type. In addition, the corresponding access popularity weight of the access record is also determined according to the data type of user input data, or according to the data type of interaction results or interaction data generated by the large language model.

[0031] In the specific execution process, when calculating the access popularity based on setting the corresponding access popularity weight for the access records, the access records can be standardized to obtain standard access records. Then, a weighted calculation can be performed based on the standard access records and the access popularity weight corresponding to each standard access record to obtain the access popularity index of each user interaction content. Alternatively, the access records can be clustered to obtain cluster records for each user's interaction content, and a weighted calculation can be performed based on each user's interaction content and the access popularity weight corresponding to each user's interaction content to obtain the access popularity index of each user's interaction content.

[0032] Specifically, in one optional implementation of this embodiment, the access popularity is calculated based on access records to obtain an access popularity index for user interaction content, including: Standardize the processing of access time records, evaluation records, dialogue interaction records and / or operation records; The standardized access time records, evaluation records, dialogue interaction records and / or operation records are weighted and calculated with their respective access popularity weights to obtain the access popularity index of each user's interaction content. or, Cluster the access time records, evaluation records, dialogue interaction records and / or operation records to obtain the cluster records of each user's interaction content; perform weighted calculation based on the cluster records of each user's interaction content and the access popularity weight corresponding to each cluster record to obtain the access popularity index of each user's interaction content.

[0033] Step S206: Correct the access popularity index based on the interactive correction parameters to obtain the corrected popularity index.

[0034] In this embodiment, considering that the modified popularity index representing the popularity of user access to user interaction content may change, such as the popularity of user access to user interaction content gradually decreasing over time, in order to more accurately represent the popularity of user access to user interaction content, an interaction correction parameter is introduced to correct the access popularity index, thereby obtaining a more accurate access popularity index.

[0035] In one optional implementation of this embodiment, a correction factor corresponding to the interaction time parameter is determined, and the access popularity index is corrected and calculated according to the correction factor to obtain the corrected popularity index. The interaction time parameter can be the duration of the interaction, the interaction time itself, or the time difference between the interaction time and the current time. To determine the correction factor corresponding to the interaction time parameter, the corresponding correction factor can be looked up in a pre-set table corresponding to interaction time parameters and correction factors. For example, if the correction factor found in the table corresponding to the duration of the user interaction is 0.9, then the product of this correction factor and the access popularity index is used as the corrected popularity index.

[0036] Furthermore, starting from the interaction correlation parameters, the corresponding correction factors can be determined, and the access popularity index can be corrected and calculated according to the correction factors to obtain the corrected popularity index. Here, the interaction correlation parameters can be obtained by inputting the interaction content of each user and the corresponding interaction input data into a large language model for correlation analysis. The correlation analysis performed by the large language model can be interaction content correlation analysis or semantic correlation analysis. The correction factors corresponding to the interaction correlation parameters can also be obtained by querying a pre-set table of correspondence between interaction correlation parameters and correction factors. Alternatively, a correction factor can be determined based on both the interaction time parameter and the interaction association parameter, and the access popularity index can be corrected and calculated according to the correction factor to obtain the corrected popularity index. The correction factor can be determined by both the interaction time parameter and the interaction association parameter, and can be obtained by querying the pre-set correspondence table of interaction time parameter, interaction association parameter and correction factor.

[0037] In practical applications, access popularity metrics and / or modified popularity metrics represent the degree to which users access user interaction content, which is also the degree to which users access the output data of the large language model. The degree to which users access the output data of the large language model can, to a certain extent, reflect the user's satisfaction with the large language model, i.e., the user's habitual preferences for the large language model. In this case, to make the output of the large language model more in line with user habitual preferences, based on the access popularity metrics and modified popularity metrics, the modified popularity metrics can be input together with the user's interactive input data (merged input) into the large language model to generate corresponding user interaction content. Optionally, access popularity metrics and / or modified popularity metrics are used to generate user interaction content after receiving the user's interactive input data, which can be obtained through inference.

[0038] Similarly, access records and user interaction input data can be input together into a large language model for inference to obtain corresponding user interaction content. That is, access records are used to generate user interaction content by inputting user interaction input data into a large language model after receiving user interaction input data.

[0039] Based on a similar implementation, the user interaction content of the aforementioned large language model can be generated by inputting user input data with stored access records, access popularity indicators, and / or modified popularity indicators into the large language model. The stored access records, access popularity indicators, and / or modified popularity indicators refer to the access records, access popularity indicators, and / or modified popularity indicators corresponding to historical user interaction content (historical interaction content). Optionally, the user interaction content includes: generating it by inputting user input data with historical interaction content and corresponding access popularity indicators and / or modified popularity indicators into the large language model.

[0040] Step S208: Generate an access popularity control for the user interaction content based on the corrected popularity index and return it to the user terminal so as to perform access interaction based on the access popularity control.

[0041] In practice, in order to transform the corrected popularity index into a visually interactive control for the user terminal and improve the efficiency of users revisiting user-interactive content, based on the obtained corrected popularity index, an access popularity control is generated according to the corrected popularity index and returned to the user terminal. The access popularity control can correspond to the user-interactive content. Subsequently, the user terminal obtains the access popularity control so that the user can perform access interaction according to the access popularity control.

[0042] The access popularity control refers to a control used to display the access popularity of user interaction content; the visual attributes of the access popularity control can be associated with the modified popularity index, for example, the color of the access popularity control can be rendered based on the modified popularity index to reflect the level of the modified popularity index of the corresponding user interaction content; the access popularity control can correspond to the display position of the user interaction content, for example, the access popularity control can correspond to the display position of the user interaction content in the current chat list, dialog box or chat page.

[0043] In practical applications, to improve the efficiency and ease of user recognition of historical interaction content, after generating the access popularity control and returning it to the user terminal, the user terminal displays the access popularity control accordingly, allowing users to access and interact based on the access popularity control. During the access interaction based on the access popularity control, the access popularity control corresponding to the location of the interaction content can be displayed on the user interaction page to visually guide the content's popularity; alternatively, when the access popularity control is triggered, access prompts related to the key semantics of the user interaction content can be displayed to assist the user in understanding the user interaction content. The following sections will explain these two interaction implementation methods in detail.

[0044] In practice, to enable users to quickly identify highly popular user interactions without having to read each one individually, thereby improving content browsing efficiency, a popularity control corresponding to the location of the user interaction content can be displayed on the user interaction page. In one optional implementation of this embodiment, access interaction is performed based on the popularity control, including: Display access popularity controls on user interaction pages that include user interaction content.

[0045] Optionally, the access popularity control is displayed after a trigger command for user interaction content or user interaction page is detected, and the display position of the access popularity control corresponds to the display position of the user interaction content.

[0046] The trigger command can be a user command on the user interaction page, such as a user swiping the user interaction page; or it can be a user command on the user interaction content, such as a user long-pressing the user interaction content.

[0047] For example, users in Figure 3 After the user interaction page is swiped, the access popularity control shown in 301 is displayed on the user interaction page; different areas of the access popularity control 301 can be rendered with different colors to correspond to different user interaction content.

[0048] In specific implementation, to enable users to determine the popularity of the currently accessed user interaction content, access prompts can be generated based on the modified popularity index of the user interaction content; in another optional implementation provided in this embodiment, access interaction is performed based on an access popularity control, including: If the access popularity control is triggered, the corresponding access prompt will be displayed according to the area where the control is triggered.

[0049] The access prompts refer to text information used to assist in understanding user interaction content. Access prompts can be generated based on the modified popularity index corresponding to the control trigger area or the key semantics of the user interaction content in the control trigger area, in order to display the access value or summary information of the user interaction content; optionally, access prompts are generated based on the modified popularity index corresponding to the control trigger area, or based on the key semantics of the user interaction content corresponding to the control trigger area.

[0050] For example, users in Figure 4 After the user interaction page shown triggers the access popularity control 401, the access prompt word 402 "Deep Reading Zone" corresponding to the trigger area can be displayed in the current trigger area of ​​the access popularity control 401 to inform the user that the user interaction content corresponding to the control trigger area is content that can be read in depth; for example, when the user... Figure 5 After the user interaction page shown triggers the access popularity control 501, the access prompt word 502 "Quick Skip Zone" corresponding to the trigger area can be displayed in the current trigger area of ​​the access popularity control 501 to inform the user that the user interaction content corresponding to the current control trigger area can be skipped without in-depth reading.

[0051] In practical applications, in order to improve the reading efficiency and information extraction convenience of users for long or multi-turn user interaction content, a summary display configuration can be returned to the user terminal according to the summary command submitted by the user terminal. After that, the user terminal obtains the summary display configuration returned by the server and performs summary interactive display on the user interaction page based on the summary display configuration. In one optional implementation of this embodiment, the method further includes: returning a summary display configuration to the user terminal according to the summary instruction of the target user interaction content uploaded by the user terminal, so as to perform summary interactive display on the user interaction page according to the summary display configuration.

[0052] Subsequently, after returning the summary display configuration to the user terminal, in order to improve the completeness of the summary content and avoid information truncation caused by fixed-length summaries, a large language model can be introduced. For example, key semantic detection can be performed using a large language model, and the end position of the summary can be determined based on the key semantics output by the large language model. In one optional implementation provided in this embodiment, after returning the summary display configuration to the user terminal, the following steps are included: Input the interaction fragments to which the target user's interaction content belongs, the access popularity index of the interaction fragments, and / or the interaction input data into the large language model to perform key semantic detection and obtain key semantics; The end position of the summary is determined based on key semantics and returned to the user terminal.

[0053] Among them, key semantics refers to the core information units obtained based on the target user's interaction content after the large language model performs key semantic detection; key semantics can be used to determine the end position of the summary; interactive input data can be interactive questions input by the user.

[0054] Specifically, during the key semantic detection process of the large language model, the input of the large language model can be any one, any two, or all three of the following: the interaction segment to which the target user's interaction content belongs, the access popularity index of the interaction segment, and the interaction input data. For example, the interaction segment to which the target user's interaction content belongs and the interaction input data can be input into the input of the large language model to perform key semantic detection and obtain key semantics; this embodiment does not limit this.

[0055] It should be noted that after determining the end position of the summary based on the key semantics output by the large language model and returning it to the user terminal, the summary content can be further determined based on the end position of the summary and returned to the user terminal. Subsequently, the summary content can be associated with the corresponding interactive question to obtain associated data. Based on obtaining the associated data, the user can access the associated data by triggering the summary identifier displayed on the user interaction page.

[0056] In addition to obtaining key semantics through key semantic detection based on a large language model and determining the end position of the summary based on the key semantics, as described above, the user can also submit the end position of the summary via the user terminal. In another optional implementation provided in this embodiment, after returning the summary display configuration to the user terminal, the following steps are included: The summary content is determined based on the target user's interaction content and the end position of the summary submitted by the user terminal; The summary content is associated with the corresponding interactive question to obtain associated data, which can be accessed by triggering the summary identifier displayed on the user interaction page.

[0057] Specifically, after returning the summary display configuration to the user terminal, the user terminal can interactively display the summary on the user interaction page. During the summary interactive display process, the user can select summary content on the user interaction page to trigger an interaction command to submit the end position of the summary to the server. Accordingly, the summary content is determined based on the target user interaction content and the end position of the summary submitted by the user terminal. Further, the summary content and the corresponding interaction question are extracted, and the summary content and the corresponding interaction question are associated and stored to obtain associated data. Subsequently, if the summary identifier triggered by the user on the user interaction display page is detected, the associated data is returned to the user terminal so that the user can access the associated data.

[0058] For example, after the server returns the summary display configuration to the user terminal, the user terminal can display the summary according to the configuration. Figure 6 The user interaction page shown displays a summary for interactive purposes, demonstrating the summary display configuration 601. Based on this summary interactive display, users can... Figure 7 The server determines the summary content 701 based on the target user's interaction content and the end position of the summary submitted by the user terminal. Subsequently, the user can also trigger the summary identifier 702 displayed on the user interaction page to access the associated data obtained by associating the summary content with the corresponding interaction question.

[0059] In practical applications, during the interaction processing of a large language model, specifically in the process of acquiring access records of user interaction content uploaded by the user terminal, as mentioned above, access records may include operation records. Here, operation records may also include summary records obtained through summary processing. Therefore, the acquired access records may include at least one of access time records, evaluation records, dialogue interaction records, and operation records, as well as summary records obtained through summary processing. The summary processing includes: according to the summary instructions of the target user interaction content uploaded by the user terminal, inputting the interaction segment to which the target user interaction content belongs, the access popularity index of the interaction segment, and / or interaction input data into the large language model for key semantic detection to obtain key semantics; and determining the summary content based on the key semantics. This embodiment will not elaborate further here.

[0060] In summary, the one or more large language model interaction processing methods provided in this embodiment involve the user terminal uploading access records of user interaction content of the large language model to the server in advance during the interaction processing of the large language model. Accordingly, access popularity is calculated based on the access records uploaded by the user terminal to obtain an access popularity index of the user interaction content. The access popularity index is then corrected based on interaction correction parameters to obtain a corrected popularity index. Furthermore, an access popularity control for the user interaction content is generated based on the corrected popularity index and returned to the user terminal, enabling the user terminal to perform access interaction based on the access popularity control. This achieves a faster access interaction for user interaction content of the large language model, thereby improving the efficiency and convenience of users accessing user interaction content.

[0061] Steps S202 to S208 provided in this embodiment can be executed by the server. It should be noted that the steps S202 to S208 executed by the server and steps S902 to S906 executed by the user terminal in the following embodiment can cooperate with each other during execution. Therefore, when reading this embodiment, please refer to the corresponding content of steps S902 to S906 provided in the following method embodiment, and when reading the following method embodiment, please refer to the corresponding content of steps S202 to S208 provided in this embodiment.

[0062] The following example uses the application of a large language model interaction processing method provided in this embodiment in a user interaction content access scenario, combined with... Figure 8 The interactive processing method for the large language model provided in this embodiment will be further explained below. Figure 8 The large language model interaction processing method applied to user interaction content access scenarios includes the following steps.

[0063] Step S804: Obtain the access records of user interaction content of the large language model uploaded by the user terminal.

[0064] Step S806: Standardize the access record items contained in the access record to obtain each standard record item.

[0065] Optionally, the access log items include at least one of the following: access time record, evaluation record, dialogue interaction record, and operation record.

[0066] Step S808: Input each standard record item into the access popularity algorithm to calculate the access popularity index.

[0067] Step S810: Determine the correction factor corresponding to the interaction time parameter, and calculate the corrected popularity index according to the correction factor.

[0068] Optionally, the interaction association parameters are obtained by inputting each user's interaction content and the corresponding interaction input data into a large language model for interaction content association analysis.

[0069] Step S812: Generate access popularity control for user interaction content based on the corrected popularity index and return it to the user terminal.

[0070] Step S820: Based on the summary instruction of the target user interaction content uploaded by the user terminal, return the summary display configuration to the user terminal.

[0071] Step S826: Determine the summary content based on the target user's interaction content and the end position of the summary submitted by the user terminal.

[0072] Step S828: Associate the summary content with the corresponding interactive question to obtain associated data; the associated data is accessed by triggering the summary identifier displayed on the user interaction page.

[0073] It should be noted that any one or more of steps S804 to S812, S820, and S826 to S828 can be combined with any one or more of steps S202 to S208 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features can be selected from steps S804 to S812, S820, and S826 to S828 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features from steps S804 to S812, S820, and S826 to S828 can be replaced with any one or more of the technical features provided in steps S202 to S208 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0074] Furthermore, it should be noted that steps S804 to S812, S820, and S826 to S828 provided in this embodiment can be executed by the server. It should be noted that the steps S804 to S812, S820, and S826 to S828 executed by the server can cooperate with steps S802, S814 to S818, and S822 to S824 executed by the user terminal in the following embodiment during execution. Therefore, when reading this embodiment, please refer to the corresponding content of steps S802, S814 to S818, and S822 to S824 provided in the following method embodiment. When reading the following method embodiment, please refer to the corresponding content of steps S804 to S812, S820, and S826 to S828 provided in this embodiment.

[0075] This specification provides one or more embodiments of a large language model interactive processing method as follows: Reference Figure 9 The large language model interaction processing method provided in this embodiment can be applied to user terminals. The method specifically includes steps S902 to S906.

[0076] Step S902: Obtain the user's trigger command for the user interaction content of the large language model.

[0077] In this embodiment, the large language model refers to a large language model that is based on the base large language model, finely tuned, and integrates tool calling capabilities to provide users with natural language dialogue interaction and contextualized services. The large language model can be a multimodal model (multimodal large language model), or a single-modal large language model. The large language model can also be a large language model deployed by an intelligent agent. In addition, the large language model can be replaced by an intelligent agent, and correspondingly, the user interaction content of the large language model can also be replaced by the user interaction content of the intelligent agent. Furthermore, the large language model and its corresponding content involved in the following can all be replaced by the intelligent agent and its corresponding content.

[0078] User interaction content of a large language model refers to the interaction results generated by the large language model during user interaction. Specifically, it is the output data obtained by the large language model after processing the user-submitted input data. User interaction with the large language model can be dialogic, text-based, or non-textual, such as inputting images, code, or files into the large language model for dialogic interaction. In addition, user interaction with the large language model can be dialogic or tool access interaction, such as triggering various tools provided by the large language model, such as document generation tools, audio and video processing tools, etc., to interact with the large language model.

[0079] Specifically, user interaction content can be the dialogue interaction content generated by at least one dialogue interaction between the user and the large language model. The dialogue interaction content of each dialogue interaction includes the dialogue output results generated by the large language model, and may also include user input data, that is, the dialogue interaction content may include dialogue questions or dialogue input entered by the user; similarly, user interaction content can also be the access interaction content generated by at least one tool access interaction between the user and the large language model, and may also include user input data entered by the user for the tool deployed by the large language model; or, user interaction content can also be the dialogue interaction content of the user and the large language model in dialogue lists, dialog boxes, or dialogue pages.

[0080] User interaction content access records refer to data used to record specific access actions or operations during the process of users accessing and viewing user interaction content. In the specific execution process, access records can be collected through the tracking code deployed in the interaction interface of the large language model.

[0081] Optionally, the access records include at least one of the following: access time records, evaluation records, dialogue interaction records, and operation records.

[0082] Among them, the access time record is used to record the time when a user accesses user interaction content, such as the duration of the user's access to user interaction content and / or the duration of selected reading; The evaluation record is used to record users' evaluation information on user interaction content, such as the number of positive evaluations (number of likes) of users on the answers to questions generated by the large language model, and / or the number of negative evaluations of users on the answers to questions; Dialogue interaction records are used to record dialogue-related information about user interactions, such as the number of times a question answer was regenerated, and / or the number of times a follow-up question was asked about the answer. Operation logs are used to record user actions related to user interaction content, such as the number or number of times a user deletes a question answer, the number or number of times a user favorites a question answer, and / or the number or number of times a user saves a summary of a question answer.

[0083] In practice, during the interactive processing of the large language model, the user's trigger command for the user interaction content of the large language model is first obtained, and then the trigger command is submitted to the server for subsequent processing.

[0084] Step S904: In response to the triggering command, obtain the access popularity control of the user interaction content from the server.

[0085] Based on the user's access records of user interaction content in the large language model, that is, based on the user's access records of accessing user interaction content, the server calculates the access popularity index of user interaction content based on the access records of user interaction content. The access popularity index is an indicator used to characterize the access popularity of user interaction content.

[0086] In the specific implementation process, considering that the access records of users accessing user interaction content may contain access records of different data dimensions, the access records can be standardized, and the obtained standardized access records can be input into the access popularity algorithm to calculate the access popularity index of user interaction content.

[0087] In one optional implementation of this embodiment, during the process of calculating access popularity based on access records to obtain access popularity indicators for user interaction content, the server performs the following operations: The access record items contained in the access record are standardized to obtain each standard record item; Each standard record item is input into the access popularity algorithm to calculate the access popularity index.

[0088] The access record item refers to each access record contained in the access record. The access record item can be collected through the instrumentation code deployed through the interaction interface of the large language model. Optionally, the access record item includes at least one of the following: access time record, evaluation record, dialogue interaction record, and operation record.

[0089] The access popularity algorithm is a weighted algorithm that calculates the access popularity by weighting each standard record item with its corresponding weight. For example, the access popularity algorithm is: HeatValue = α×T + β×L + γ×F; where HeatValue is the access popularity index, T, L, and F are access time records, evaluation records, and dialogue interaction records, respectively, and α, β, and γ are the weights of the access time records, evaluation records, and dialogue interaction records, respectively.

[0090] Optionally, the access popularity metric is obtained by calculating the access popularity based on the access records of user interaction content.

[0091] In practical applications, the impact of access records from different data dimensions on access popularity may vary. To improve the accuracy of access popularity calculation, corresponding access popularity weights can be set for access records from different data dimensions. Furthermore, to make access popularity calculation more consistent with the actual interaction scenarios of large language models, corresponding access popularity weights can be set for the same data dimension in different interaction scenarios. For example, one access popularity weight can be set for dialogue interaction scenarios, and another access popularity weight can be set for tool access interaction scenarios. In this case, the corresponding access popularity weight of the access record is determined according to the interaction scenario type. In addition, the corresponding access popularity weight of the access record is also determined according to the data type of user input data, or according to the data type of interaction results or interaction data generated by the large language model.

[0092] In the specific execution process, the server can standardize the access records to obtain standard access records based on the corresponding access popularity weights set for the access records, and perform weighted calculations based on the standard access records and the access popularity weights corresponding to each standard access record to obtain the access popularity index of each user interaction content. Alternatively, the access records can be clustered to obtain cluster records for each user's interaction content, and a weighted calculation can be performed based on each user's interaction content and the access popularity weight corresponding to each user's interaction content to obtain the access popularity index of each user's interaction content.

[0093] Specifically, in one optional implementation of this embodiment, during the process of calculating access popularity based on access records to obtain the access popularity index of user interaction content, the server performs the following operations: Standardize the processing of access time records, evaluation records, dialogue interaction records and / or operation records; The standardized access time records, evaluation records, dialogue interaction records and / or operation records are weighted and calculated with their respective access popularity weights to obtain the access popularity index of each user's interaction content. or, Cluster the access time records, evaluation records, dialogue interaction records and / or operation records to obtain the cluster records of each user's interaction content; perform weighted calculation based on the cluster records of each user's interaction content and the access popularity weight corresponding to each cluster record to obtain the access popularity index of each user's interaction content.

[0094] Subsequently, considering that the modified popularity index representing the popularity of user access to user interaction content may change, such as the popularity of user access to user interaction content gradually decreasing over time, in order to more accurately represent the popularity of user access to user interaction content, an interaction correction parameter is introduced to modify the access popularity index, thereby obtaining a more accurate access popularity index.

[0095] In one optional implementation of this embodiment, a correction factor corresponding to the interaction time parameter is determined, and the access popularity index is corrected and calculated according to the correction factor to obtain the corrected popularity index. The interaction time parameter can be the duration of the interaction, the interaction time itself, or the time difference between the interaction time and the current time. To determine the correction factor corresponding to the interaction time parameter, the corresponding correction factor can be looked up in a pre-set table corresponding to interaction time parameters and correction factors. For example, if the correction factor found in the table corresponding to the duration of the user interaction is 0.9, then the product of this correction factor and the access popularity index is used as the corrected popularity index.

[0096] Optionally, the access popularity control is generated based on the modified popularity index obtained by modifying the access popularity index of user interaction content.

[0097] Furthermore, starting from the interaction correlation parameters, the corresponding correction factors can be determined, and the access popularity index can be corrected and calculated according to the correction factors to obtain the corrected popularity index. Here, the interaction correlation parameters can be obtained by inputting the interaction content of each user and the corresponding interaction input data into a large language model for correlation analysis. The correlation analysis performed by the large language model can be interaction content correlation analysis or semantic correlation analysis. The correction factors corresponding to the interaction correlation parameters can also be obtained by querying a pre-set table of correspondence between interaction correlation parameters and correction factors. Alternatively, a correction factor can be determined based on both the interaction time parameter and the interaction association parameter, and the access popularity index can be corrected and calculated according to the correction factor to obtain the corrected popularity index. The correction factor can be determined by both the interaction time parameter and the interaction association parameter, and can be obtained by querying the pre-set correspondence table of interaction time parameter, interaction association parameter and correction factor.

[0098] In practical applications, access popularity metrics and / or modified popularity metrics represent the degree to which users access user interaction content, which is also the degree to which users access the output data of the large language model. The degree to which users access the output data of the large language model can, to a certain extent, reflect the user's satisfaction with the large language model, i.e., the user's habitual preferences for the large language model. In this case, to make the output of the large language model more in line with user habitual preferences, based on the access popularity metrics and modified popularity metrics, the modified popularity metrics can be input together with the user's interactive input data (merged input) into the large language model to generate corresponding user interaction content. Optionally, access popularity metrics and / or modified popularity metrics are used to generate user interaction content after receiving the user's interactive input data, which can be obtained through inference.

[0099] Similarly, access records and user interaction input data can be input together into a large language model for inference to obtain corresponding user interaction content. That is, access records are used to generate user interaction content by inputting user interaction input data into a large language model after receiving user interaction input data.

[0100] Based on a similar implementation, the user interaction content of the aforementioned large language model can be generated by inputting user input data with stored access records, access popularity indicators, and / or modified popularity indicators into the large language model. The stored access records, access popularity indicators, and / or modified popularity indicators refer to the access records, access popularity indicators, and / or modified popularity indicators corresponding to historical user interaction content (historical interaction content). Optionally, the user interaction content includes: generating it by inputting user input data with historical interaction content and corresponding access popularity indicators and / or modified popularity indicators into the large language model.

[0101] In practice, after obtaining access popularity metrics and correcting them to obtain corrected popularity metrics, the server can further generate access popularity controls for user interaction content based on the corrected popularity metrics and return them to the user terminal; correspondingly, in response to the trigger command, the server retrieves the access popularity controls for user interaction content.

[0102] Step S906: Display the access popularity control to switch the interactive content according to user operation.

[0103] In the case of retrieving the access popularity control from the server, the access popularity control is displayed here, and the user interaction content is switched according to the user's operation on the access popularity control.

[0104] The access popularity control refers to a control used to display the access popularity of user interaction content; the visual attributes of the access popularity control can be associated with the modified popularity index. For example, the color of the access popularity control can be rendered based on the modified popularity index to reflect the level of the modified popularity index of the corresponding user interaction content.

[0105] Optionally, the display position of the access popularity control corresponds to the display position of the user interaction content. For example, the access popularity control can correspond to the display position of the user interaction content in the current chat list, dialog box, or chat page.

[0106] In practical applications, in order to improve the efficiency of users in recognizing historical interaction content and the convenience of operation, the access popularity control can be displayed in the display position corresponding to the user interaction content on the user interaction page during the display process. In addition, access prompts can be displayed after the access popularity control is triggered to achieve visual guidance on the popularity of content. In one optional implementation of this embodiment, the display processing of the access popularity control includes: Display access popularity controls on user interaction pages that include user interaction content; If the access popularity control is triggered, the corresponding access prompt will be displayed according to the area where the control is triggered.

[0107] The access prompts refer to text information used to assist in understanding user interaction content. Access prompts can be generated based on the modified popularity index corresponding to the control trigger area or the key semantics of the user interaction content in the control trigger area, in order to display the access value or summary information of the user interaction content; optionally, access prompts are generated based on the modified popularity index corresponding to the control trigger area, or based on the key semantics of the user interaction content corresponding to the control trigger area.

[0108] For example, users in Figure 3 After the user interaction page is swiped, the access popularity control shown in 301 is displayed on the user interaction page; different areas of the access popularity control 301 can be rendered with different colors to correspond to different user interaction content.

[0109] For example, users in Figure 4 After the user interaction page shown triggers the access popularity control 401, the access prompt word 402 "Deep Reading Zone" corresponding to the trigger area can be displayed in the current trigger area of ​​the access popularity control 401 to inform the user that the user interaction content corresponding to the control trigger area is content that can be read in depth; for example, when the user... Figure 5After the user interaction page shown triggers the access popularity control 501, the access prompt word 502 "Quick Skip Zone" corresponding to the trigger area can be displayed in the current trigger area of ​​the access popularity control 501 to inform the user that the user interaction content corresponding to the current control trigger area can be skipped without in-depth reading.

[0110] In practical applications, in order to improve the reading efficiency and information extraction convenience of users for long or multi-turn user interaction content, users can also submit summary commands based on the user interaction page, so that the user terminal can obtain the summary display configuration returned by the server and perform summary interactive display on the user interaction page based on the summary display configuration; In one optional implementation of this embodiment, the method further includes: obtaining a summary instruction from the user regarding the target user interaction content on the user interaction page and uploading it to the server; The summary display is performed on the user interaction page according to the summary display configuration returned by the server.

[0111] For example, users targeting Figure 6 After the target user interaction content submits a summary command, the system retrieves the summary command and uploads it to the server, displaying the summary according to the server's returned summary configuration. Figure 6 The user interaction page shown displays a summary interactive display to demonstrate the summary display configuration 601.

[0112] Subsequently, after the server returns the summary display configuration to the user terminal, in order to improve the completeness of the summary content and avoid information truncation caused by fixed-length summaries, the server can introduce a large language model. For example, it can perform key semantic detection through a large language model and determine the end position of the summary based on the key semantics output by the large language model. In an optional implementation of this embodiment, after the summary is interactively displayed on the user interaction page according to the summary display configuration returned by the server, it also includes: The summary display ends based on the end position of the summary returned by the server.

[0113] Optionally, the end position of the abstract can be determined by inputting the target user's interactive content to the interaction segment, the access popularity index of the interaction segment, and / or the interaction input data into the large language model for key semantic detection.

[0114] Among them, key semantics refers to the core information units obtained based on the target user's interaction content after the large language model performs key semantic detection; key semantics can be used to determine the end position of the summary; interactive input data can be interactive questions input by the user.

[0115] Specifically, the server inputs at least one of the following into a large language model: the interaction segment to which the target user's interaction content belongs, the access popularity index of the interaction segment, and the interaction input data. The model performs key semantic detection to obtain key semantics, determines the end position of the summary based on the key semantics, and returns it to the user terminal. After that, the summary display ends based on the end position of the summary returned by the server.

[0116] It should be noted that during the key semantic detection process of the large language model, the input of the large language model can be any one, any two, or all three of the following: the interaction segment to which the target user's interaction content belongs, the access popularity index of the interaction segment, and the interaction input data. For example, the interaction segment to which the target user's interaction content belongs and the interaction input data can be input into the input of the large language model to perform key semantic detection and obtain key semantics; this embodiment does not limit this.

[0117] It should also be noted that after the server determines the end position of the summary based on the key semantics output by the large language model and returns it to the user terminal, the server can further determine the summary content based on the end position of the summary and return the summary content to the user terminal. After that, the server can associate the summary content with the corresponding interactive question to obtain associated data. Based on obtaining the associated data, the user can access the associated data by triggering the summary identifier displayed on the user interaction page.

[0118] In addition to obtaining key semantics through key semantic detection based on a large language model and determining the end position of the summary based on the key semantics, as described above, the user can also submit the end position of the summary on the user terminal. In another optional implementation of this embodiment, after displaying the summary interactively on the user interaction page according to the summary display configuration returned by the server, it also includes: The summary display ends based on the end position of the summary submitted by the user, and the end position of the summary is uploaded to the server to determine the summary content and associate it with the corresponding interactive question for storage.

[0119] Optionally, associated data obtained from associated storage can be accessed by triggering a summary identifier displayed on the user interaction page.

[0120] Specifically, after the summary interactive display, the user can select the summary content on the user interaction page to trigger an interactive command to submit the end position of the summary to the server. Subsequently, the server determines the summary content based on the target user's interactive content and the end position of the summary submitted by the user's terminal. Further, the server can extract the summary content and the corresponding interactive question, and associate the summary content with the corresponding interactive question to obtain associated data. Afterward, the user can access the associated data obtained by triggering the summary identifier on the user interaction display page.

[0121] For example, based on the interactive display of summaries, users can... Figure 7 The server determines the summary content 701 based on the target user's interaction content and the end position of the summary submitted by the user terminal. Subsequently, the user can also trigger the summary identifier 702 displayed on the user interaction page to access the associated data obtained by associating the summary content with the corresponding interaction question.

[0122] In summary, the one or more large language model interaction processing methods provided in this embodiment, during the interaction processing of the large language model, obtain the user's trigger command for the user interaction content of the large language model, and upload the access record of the user interaction content of the large language model to the server. Correspondingly, the server calculates the access popularity index of the user interaction content based on the access record uploaded by the user terminal, and corrects the access popularity index based on the interaction correction parameters to obtain a corrected popularity index. Furthermore, based on the corrected popularity index, an access popularity control of the user interaction content is generated and returned to the user terminal. After obtaining the access popularity control of the user interaction content from the server, the access popularity control is displayed to switch the interaction content according to the user's operation. In this way, a quick access interaction is achieved for the user interaction content of the large language model, thereby improving the efficiency and convenience of the user's access to the user interaction content.

[0123] The following example uses the application of a large language model interaction processing method provided in this embodiment in a user interaction content access scenario, combined with... Figure 8 The interactive processing method for the large language model provided in this embodiment will be further explained below. Figure 8 The large language model interaction processing method applied to user interaction content access scenarios includes the following steps.

[0124] Step S802: Obtain the user's trigger command for the user interaction content of the large language model, and upload the access record of the user interaction content of the large language model to the server.

[0125] Step S814: Obtain the access popularity control of user interaction content from the server.

[0126] Step S816: Display the access popularity control.

[0127] After step S816 is executed, if the access popularity control is triggered, the corresponding access prompt words are displayed according to the control trigger area; wherein, the access prompt words are generated according to the corrected popularity index corresponding to the control trigger area, or according to the key semantics of the user interaction content corresponding to the control trigger area.

[0128] Step S818: Obtain the summary instruction from the user regarding the target user interaction content on the user interaction page and upload it to the server.

[0129] Step S822: Display the summary interactively on the user interaction page according to the summary display configuration returned by the server.

[0130] Step S824: End the summary display according to the end position of the summary submitted by the user, and upload the end position of the summary to the server.

[0131] It should be noted that any one or more of steps S802, S814 to S818, and S822 to S824 can be combined with any one or more of steps S902 to S906 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S802, S814 to S818, and S822 to S824 can be selected and combined with any one or more technical features provided in steps S902 to S906 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S802, S814 to S818, and S822 to S824 can also be replaced with any one or more technical features provided in steps S902 to S906 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0132] This specification provides an embodiment of a large language model interactive processing device as follows: In the above embodiments, a method for interactive processing of large language models is provided, and correspondingly, a device for interactive processing of large language models is also provided, which will be described below with reference to the accompanying drawings.

[0133] Reference Figure 10 This illustration shows a schematic diagram of an embodiment of a large language model interactive processing device provided in this embodiment.

[0134] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0135] This embodiment provides a large language model interactive processing device, including: The record acquisition module 1002 is configured to acquire access records of user interaction content of a large language model uploaded by the user terminal; The indicator calculation module 1004 is configured to calculate the access popularity based on the access records to obtain the access popularity indicator of the user interaction content. The indicator correction module 1006 is configured to perform correction processing on the access popularity indicator based on the interactive correction parameters to obtain the corrected popularity indicator. The control return module 1008 is configured to generate an access popularity control for the user interaction content based on the corrected popularity index and return it to the user terminal so as to perform access interaction based on the access popularity control.

[0136] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0137] Another embodiment of the large language model interactive processing device provided in this specification is as follows: In the above embodiments, another method for large language model interaction processing is provided, and correspondingly, another device for large language model interaction processing is also provided, which will be described below with reference to the accompanying drawings.

[0138] Reference Figure 11 This illustration shows a schematic diagram of another embodiment of the large language model interactive processing device provided in this embodiment.

[0139] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0140] This embodiment provides a large language model interactive processing device, including: The instruction acquisition module 1102 is configured to acquire the trigger instructions of the user's interaction content with the large language model; The control acquisition module 1104 is configured to, in response to the triggering instruction, acquire the access popularity control of the user interaction content from the server; the access popularity control is generated based on the corrected popularity index obtained by correcting the access popularity index of the user interaction content, and the access popularity index is obtained by calculating the access popularity based on the access records of the user interaction content. The control display module 1106 is configured to display the access popularity control to switch interactive content based on user operations.

[0141] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0142] This specification provides an embodiment of a large language model interactive processing device as follows: Corresponding to the large language model interactive processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a large language model interactive processing device, which is used to execute the large language model interactive processing method provided above. Figure 12 This is a schematic diagram of the structure of a large language model interactive processing provided for one or more embodiments of this specification.

[0143] This embodiment provides a large language model interactive processing device, including: like Figure 12As shown, device 1200 mainly consists of a communication interface 1202, a user interface 1204, a processor 1206, and a data storage 1208. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 1210. The communication interface 1202 enables device 1200 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 1202 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 1202 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 1202 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 1202 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 1204 includes receiving user input and providing output to the user. Therefore, user interface 1204 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 1204 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 1204 may include software, circuitry, or other forms of logic capable of transmitting data to and receiving data from external user input / output devices. Additionally or alternatively, device 1200 may support remote access from other devices via communication interface 1202 or another physical interface (not shown). User interface 1204 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 1204 may also be configured as a display device for rendering or displaying text fragments.

[0144] Processor 1206 may include one or more general-purpose processors and / or dedicated processors. Data storage 1208 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 1206. Data storage 1208 may include removable and non-removable components.

[0145] Processor 1206 is capable of executing program instructions 1218 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 1208 to perform the various functions described herein. Data storage 1208 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 1200, enable device 1200 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 1218 by processor 1206 may result in processor 1206 using data 1212. For example, program instructions 1218 may include an operating system 1222 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 1200 and one or more application programs 1220 (e.g., a browser, social application, or game application). Similarly, data 1212 may include operating system data 1216 and application data 1214. Operating system data 1216 is primarily accessible to operating system 1222, while application data 1214 is primarily accessible to one or more application programs 1220. Application data 1214 may reside in a file system visible or hidden to the user of device 1200. Application 1220 may communicate with operating system 1212 via one or more application programming interfaces (APIs). These APIs facilitate application 1220 reading and / or writing application data 1214, transmitting or receiving information via communication interface 1202, receiving or displaying information on user interface 1204, etc. In some terms, application 1220 may be simply referred to as "app". Furthermore, application 1220 may be downloaded to device 1200 through one or more online app stores or app markets. However, applications may also be installed on device 1200 in other ways, such as through a web browser or a physical interface on device 1200 (e.g., a USB port).

[0146] In one specific embodiment, the large language model interactive processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the large language model interactive processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: Retrieve access records of user interaction content from large language models uploaded by user terminals; The access popularity is calculated based on the access records to obtain the access popularity index of the user interaction content; The access popularity index is obtained by correcting the access popularity index based on the interactive correction parameters. Based on the modified popularity index, an access popularity control for the user interaction content is generated and returned to the user terminal so that access interaction can be performed according to the access popularity control.

[0147] Another embodiment of the large language model interactive processing device provided in this specification is as follows: Corresponding to the other large language model interactive processing method described above, based on the same technical concept, one or more embodiments of this specification also provide another large language model interactive processing device, which is used to execute the other large language model interactive processing method provided above. Figure 13 This is a schematic diagram of the structure of another large language model interactive processing device provided in one or more embodiments of this specification.

[0148] This embodiment provides a large language model interactive processing device, including: like Figure 13As shown, device 1300 mainly consists of a communication interface 1302, a user interface 1304, a processor 1306, and a data storage 1308. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 1310. The communication interface 1302 enables device 1300 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 1302 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 1302 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 1302 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 1302 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 1304 includes receiving user input and providing output to the user. Therefore, user interface 1304 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 1304 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 1304 may include software, circuitry, or other forms of logic capable of transmitting data to and receiving data from external user input / output devices. Additionally or alternatively, device 1300 may support remote access from other devices via communication interface 1302 or another physical interface (not shown). User interface 1304 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 1304 may also be configured as a display device for rendering or displaying text fragments.

[0149] Processor 1306 may include one or more general-purpose processors and / or special-purpose processors. Data storage 1308 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 1306. Data storage 1308 may include removable and non-removable components.

[0150] Processor 1306 is capable of executing program instructions 1318 (e.g., compiled or uncompiled program logic and / or machine code) stored in data store 1308 to perform the various functions described herein. Data store 1308 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 1300, enable device 1300 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 1318 by processor 1306 may result in processor 1306 using data 1312. For example, program instructions 1318 may include an operating system 1322 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 1300 and one or more application programs 1320 (e.g., a browser, social application, or game application). Similarly, data 1312 may include operating system data 1316 and application data 1314. Operating system data 1316 is primarily accessible to operating system 1322, while application data 1314 is primarily accessible to one or more application programs 1320. Application data 1314 may reside in a file system visible or hidden to the user of device 1300. Application 1320 may communicate with operating system 1312 via one or more application programming interfaces (APIs). These APIs facilitate application 1320 reading and / or writing application data 1314, transmitting or receiving information via communication interface 1302, receiving or displaying information on user interface 1304, etc. In some terms, application 1320 may be simply referred to as an "app". Furthermore, application 1320 may be downloaded to device 1300 through one or more online app stores or app markets. However, applications may also be installed on device 1300 in other ways, such as through a web browser or a physical interface on device 1300 (e.g., a USB port).

[0151] In one specific embodiment, the large language model interactive processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the large language model interactive processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: Obtain the trigger commands for user interaction content related to the large language model; In response to the trigger command, the access popularity control of the user interaction content is obtained from the server; the access popularity control is generated based on the corrected popularity index obtained by correcting the access popularity index of the user interaction content, and the access popularity index is obtained by calculating the access popularity based on the access records of the user interaction content. The access popularity control is displayed to switch interactive content based on user actions.

[0152] This specification provides an embodiment of a computer-readable storage medium as follows: Corresponding to the large language model interactive processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0153] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process: Retrieve access records of user interaction content from large language models uploaded by user terminals; The access popularity is calculated based on the access records to obtain the access popularity index of the user interaction content; The access popularity index is obtained by correcting the access popularity index based on the interactive correction parameters. Based on the modified popularity index, an access popularity control for the user interaction content is generated and returned to the user terminal so that access interaction can be performed according to the access popularity control.

[0154] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a large language model interactive processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0155] Another embodiment of a computer-readable storage medium provided in this specification is as follows: Corresponding to the other large language model interaction processing method described above, based on the same technical concept, one or more embodiments of this specification also provide another computer-readable storage medium.

[0156] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process: Obtain the trigger commands for user interaction content related to the large language model; In response to the trigger command, the access popularity control of the user interaction content is obtained from the server; the access popularity control is generated based on the corrected popularity index obtained by correcting the access popularity index of the user interaction content, and the access popularity index is obtained by calculating the access popularity based on the access records of the user interaction content. The access popularity control is displayed to switch interactive content based on user actions.

[0157] It should be noted that the embodiments of another computer-readable storage medium described in this specification and the embodiments of another large language model interaction processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0158] This specification provides an example of a computer program product as follows: Corresponding to the large language model interactive processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0159] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps: Retrieve access records of user interaction content from large language models uploaded by user terminals; The access popularity is calculated based on the access records to obtain the access popularity index of the user interaction content; The access popularity index is obtained by correcting the access popularity index based on the interactive correction parameters. Based on the modified popularity index, an access popularity control for the user interaction content is generated and returned to the user terminal so that access interaction can be performed according to the access popularity control.

[0160] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a large language model interaction processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0161] Another example of a computer program product provided in this specification is as follows: Corresponding to the other large language model interaction processing method described above, based on the same technical concept, one or more embodiments of this specification also provide another computer program product.

[0162] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps: Obtain the trigger commands for user interaction content related to the large language model; In response to the trigger command, the access popularity control of the user interaction content is obtained from the server; the access popularity control is generated based on the corrected popularity index obtained by correcting the access popularity index of the user interaction content, and the access popularity index is obtained by calculating the access popularity based on the access records of the user interaction content. The access popularity control is displayed to switch interactive content based on user actions.

[0163] It should be noted that the embodiments of another computer program product described in this specification and the embodiments of another large language model interaction processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0164] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments are all similar to the method embodiments, so the descriptions are relatively simple. For reading the relevant content of the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments, please refer to the description of the method embodiments.

[0165] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps, and does not represent the only execution order. Therefore, when the claims involve method steps, modifications to the order of such steps, or parallel execution between steps, are also within the scope of protection of the claims. This specification uses specific terms to describe embodiments of this specification. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0166] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0167] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0168] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0169] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0170] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0171] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0176] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0177] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0178] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0179] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in virtual computing environments where tasks are performed by remote processing devices connected via a communication network. In virtual computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0180] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A method for interactive processing of large language models, comprising: Retrieve access records of user interaction content from large language models uploaded by user terminals; The access popularity is calculated based on the access records to obtain the access popularity index of the user interaction content; The access popularity index is obtained by correcting the access popularity index based on the interactive correction parameters. Based on the modified popularity index, an access popularity control for the user interaction content is generated and returned to the user terminal so that access interaction can be performed according to the access popularity control.

2. The large language model interaction processing method according to claim 1, wherein the step of calculating the access popularity based on the access records to obtain the access popularity index of the user interaction content includes: The access record items contained in the access record are standardized to obtain each standard record item; The access popularity index is obtained by inputting the standard record items into the access popularity algorithm.

3. The large language model interaction processing method according to claim 2, wherein the access record item is collected and obtained through the tracking code deployed on the interaction interface of the large language model; wherein The access record items include at least one of the following: access time record, evaluation record, dialogue interaction record, and operation record.

4. The large language model interaction processing method according to claim 1, wherein the step of calculating the access popularity based on the access records to obtain the access popularity index of the user interaction content includes: Cluster the access time records, evaluation records, dialogue interaction records and / or operation records to obtain the cluster records of each user's interaction content; The access popularity index of each user interaction content is obtained by weighting the cluster records of each user interaction content and the access popularity weights corresponding to each cluster record.

5. The large language model interaction processing method according to claim 1, wherein the step of correcting the access popularity index based on interaction correction parameters to obtain the corrected popularity index includes: Determine the correction factors corresponding to the interaction time parameters and / or interaction association parameters, and calculate the correction of the access popularity index according to the correction factors to obtain the corrected popularity index. The interaction association parameters are obtained by inputting each user's interaction content and the corresponding interaction input data into a large language model for interaction content association analysis.

6. The large language model interaction processing method according to claim 1, wherein the access popularity index and / or modified popularity index are used to obtain user interaction content by inference with the large language model after receiving the user's interaction input data and the interaction input data.

7. The large language model interaction processing method according to claim 1, wherein the step of performing access interaction based on the access popularity control includes: The access popularity control is displayed on the user interaction page that contains the user interaction content. The access popularity control is displayed after a trigger command is detected for the user interaction content or the user interaction page, and the display position of the access popularity control corresponds to the display position of the user interaction content.

8. The large language model interaction processing method according to claim 7, wherein the accessing interaction according to the access heat control further comprises: if the access heat control is triggered, displaying a corresponding access prompt word according to a control trigger area; the access prompt word is generated according to a revised heat index corresponding to the control trigger area, or according to a key semantic of a user interaction content corresponding to the control trigger area.

9. The large language model interaction processing method according to claim 1, further comprising: returning a summary display configuration to the user terminal according to a summary instruction of the target user interaction content uploaded by the user terminal, so as to display a summary interaction on a user interaction page according to the summary display configuration.

10. The large language model interaction processing method according to claim 9, wherein after the operation of returning the summary display configuration to the user terminal, further comprising: inputting an interaction segment to which the target user interaction content belongs, an access heat index of the interaction segment and / or interaction input data into a large language model to detect a key semantic, and obtaining the key semantic; determining a summary end position according to the key semantic and returning the summary end position to the user terminal.

11. The large language model interaction processing method according to claim 9, wherein after the operation of returning the summary display configuration to the user terminal, further comprising: determining a summary content according to the target user interaction content and a summary end position submitted by the user terminal; storing the summary content and a corresponding interaction question to obtain associated data, so as to access the associated data by triggering a summary identifier displayed on the user interaction page.

12. A large language model interaction processing method, comprising: obtaining a trigger instruction of user interaction content of a user to a large language model; in response to the trigger instruction, obtaining an access heat control of the user interaction content from a server; the access heat control is generated according to a revised heat index obtained by revising an access heat index of the user interaction content; the access heat index is obtained by calculating an access heat according to an access record of the user interaction content; performing display processing of the access heat control to switch the interaction content according to a user operation.

13. The large language model interaction processing method according to claim 12, wherein the display processing of the access heat control comprises: displaying the access heat control on a user interaction page containing the user interaction content; the display position of the access heat control corresponds to the display position of the user interaction content; if the access heat control is triggered, displaying a corresponding access prompt word according to a control trigger area; the access prompt word is generated according to a revised heat index corresponding to the control trigger area, or according to a key semantic of a user interaction content corresponding to the control trigger area.

14. The large language model interaction processing method according to claim 12, further comprising: obtaining a summary instruction of a target user interaction content in the user interaction page by the user and uploading the summary instruction to the server. According to the summary display configuration returned by the server, the summary interaction display is performed on the user interaction page.

15. The large language model interaction processing method according to claim 14, further comprising, after the step of performing the summary interaction display according to the summary display configuration returned by the server on the user interaction page: ending the summary display according to the summary end position returned by the server; the summary end position is determined according to the interactive segment to which the target user interaction content belongs, the access heat index of the interactive segment, and / or the key semantic detection of the interactive input data input large language model; or, ending the summary display according to the summary end position submitted by the user, and uploading the summary end position to the server to determine the summary content and store it in association with the corresponding interactive question; the association data obtained by the association storage is accessed by triggering the summary identifier displayed on the user interaction page.

16. A large language model interaction processing apparatus, comprising: a record acquisition module configured to acquire access records of user interaction content of a large language model uploaded by a user terminal; an index calculation module configured to calculate access heat according to the access records to obtain an access heat index of the user interaction content; an index correction module configured to correct the access heat index based on an interactive correction parameter to obtain a corrected heat index; a control return module configured to generate an access heat control of the user interaction content according to the corrected heat index and return it to the user terminal, so that access interaction is performed according to the access heat control.

17. A large language model interaction processing apparatus, comprising: an instruction acquisition module configured to acquire a trigger instruction of user interaction content of a large language model by a user; a control acquisition module configured to acquire an access heat control of the user interaction content from a server in response to the trigger instruction; the access heat control is generated according to a corrected heat index obtained by correcting an access heat index of the user interaction content; the access heat index is obtained by calculating access heat according to access records of the user interaction content; a control display module configured to display the access heat control to switch the interaction content according to user operation.

18. A large language model interaction processing device, comprising: a processor; and a memory configured to store computer executable instructions, which when executed cause the processor to: acquire access records of user interaction content of a large language model uploaded by a user terminal; calculate access heat according to the access records to obtain an access heat index of the user interaction content; correct the access heat index based on an interactive correction parameter to obtain a corrected heat index; generate an access heat control of the user interaction content according to the corrected heat index and return it to the user terminal, so that access interaction is performed according to the access heat control.

19. A large language model interaction processing device, comprising: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: obtain a trigger instruction of user interaction content of a user for a large language model; in response to the trigger instruction, obtain an access heat control of the user interaction content from a server; the access heat control is generated according to a modified heat index obtained by modifying an access heat index of the user interaction content; the access heat index is obtained by calculating the access heat according to the access record of the user interaction content; perform display processing of the access heat control to switch the interaction content according to user operation.

20. A computer-readable storage medium for storing computer-executable instructions, which, when executed, implement the steps of the method of claim 1 or 12.

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