Information processing method and apparatus, and device and storage medium

Abstract

The embodiments of the present disclosure relate to an information processing method and apparatus, and a device and a storage medium. The method provided herein comprises: acquiring target response content of a target model for a target query item, wherein the target response content indicates a set of objects determined on the basis of the target query item; on the basis of the target query item, determining at least one query constraint; on the basis of the target response content and the at least one query constraint, generating a set of additional query items; and on the basis of a set of additional response content generated by the target model for the set of additional query items, determining an evaluation for the target response content. In this way, the embodiments of the present disclosure can support a user in more efficiently determining the evaluation of target response content for a target query term.

Description

Information processing method, device, equipment and storage medium Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to information processing methods, apparatuses, devices, and computer-readable storage media. Background Art

[0002] With the development of computer technology, various generative models have been applied to various aspects of people's lives. In addition, some developers can also customize various types of intelligent agents or robots (bots) based on models to provide people with services such as conversation and search.

[0003] Summary of the Invention

[0004] In a first aspect of the present disclosure, a method for information processing is provided. The method includes: obtaining target response content from a target model for a target query term, the target response content indicating a set of objects determined based on the target query term; determining at least one query constraint based on the target query term; generating a set of additional query terms based on the target response content and the at least one query constraint; and determining an evaluation of the target response content based on the set of additional response content generated by the target model for the set of additional query terms.

[0005] In a second aspect of the present disclosure, a device for information processing is provided. The device includes: a content acquisition module configured to acquire target response content from a target model for a target query term, the target response content indicating a set of objects determined based on the target query term; a constraint determination module configured to determine at least one query constraint based on the target query term; a content generation module configured to generate a set of additional query terms based on the target response content and the at least one query constraint; and an evaluation determination module configured to determine an evaluation of the target response content based on the set of additional response content generated by the target model for the set of additional query terms.

[0006] In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0007] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to implement the method of the first aspect.

[0008] In a fifth aspect of the present disclosure, a computer program product is provided, which includes computer-executable instructions, which, when executed by a processor, implement the method according to the first aspect of the present disclosure.

[0009] It should be understood that the content described in this summary section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0011] FIG1 shows a schematic diagram of an example environment in which embodiments according to the present disclosure may be implemented;

[0012] FIG2 shows a flowchart of an example process of information processing according to some embodiments of the present disclosure;

[0013] FIG3 is a schematic diagram showing an example process of information processing according to some embodiments of the present disclosure;

[0014] FIG4 shows a schematic structural block diagram of an example apparatus for information processing according to some embodiments of the present disclosure; and

[0015] FIG5 shows a block diagram of an electronic device capable of implementing various embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0017] It should be noted that the titles of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and any type of embodiment may be included under any section / subsection. Furthermore, the embodiments described in any section / subsection may be combined in any manner with any other embodiments described in the same section / subsection and / or in different sections / subsections.

[0018] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below. The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

[0019] The embodiments of the present disclosure may involve user data, data acquisition and / or use, etc. These aspects shall comply with the corresponding laws, regulations and relevant provisions. In the embodiments of the present disclosure, all data collection, acquisition, processing, processing, forwarding, use, etc. are carried out on the premise that the user is aware of and confirms them. Accordingly, when implementing the various embodiments of the present disclosure, the types, scope of use, and usage scenarios of the data or information that may be involved should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with the relevant laws and regulations. The specific notification and / or authorization method may vary according to the actual situation and application scenario, and the scope of the present disclosure is not limited in this respect.

[0020] If this specification and the solutions in the examples involve the processing of personal information, such processing will be done only with a legitimate basis (such as with the consent of the subject of personal information or as necessary for the performance of a contract) and only within the prescribed or agreed scope. A user's refusal to process personal information other than that required for basic functions will not affect the user's use of basic functions.

[0021] As discussed above, intelligent agents or bots can provide various services to people. For example, people can use them to search for information. However, to verify the accuracy of the results provided by the model, agent, or bot, people need to manually verify the results, which is time-consuming and expensive.

[0022] Embodiments of the present disclosure provide an information processing scheme. According to the scheme, target response content of a target model for a target query term is obtained, the target response content indicating a set of objects determined based on the target query term; at least one query constraint is determined based on the target query term; a set of additional query terms is generated based on the target response content and the at least one query constraint; and an evaluation of the target response content is determined based on the set of additional response content generated by the target model for the set of additional query terms.

[0023] In this way, the embodiments of the present disclosure can support users in evaluating target response content for target query items through models, thereby more efficiently screening out target response content that fails the test.

[0024] Various example implementations of this solution are described in detail below in conjunction with the accompanying drawings.

[0025] Sample Environment

[0026] FIG1 shows a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. As shown in FIG1 , the example environment 100 may include an electronic device 110 .

[0027] In this example environment 100, electronic device 110 may run an application 120 that supports interface interaction. Application 120 may be any suitable type of application for interface interaction, examples of which may include, but are not limited to, video applications, social applications, or other suitable applications. User 140 may interact with application 120 via electronic device 110 and / or its attached devices.

[0028] In the environment 100 of FIG. 1 , if the application 120 is in an active state, the electronic device 110 may present an interface 150 for supporting interface interaction through the application 120 .

[0029] In some embodiments, the electronic device 110 communicates with the server 130 to enable the provision of services for the application 120. The electronic device 110 can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a handheld computer, a portable game terminal, a VR / AR device, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, the electronic device 110 can also support any type of interface for the user (such as a "wearable" circuit, etc.).

[0030] The server 130 may be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The server 130 may include, for example, a computing system / server such as a mainframe, an edge computing node, a computing device in a cloud environment, etc. The server 130 may provide background services for the application 120 that supports content presentation in the electronic device 110.

[0031] A communication connection may be established between the server 130 and the electronic device 110. The communication connection may be established in a wired or wireless manner. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect. In the embodiments of the present disclosure, the server 130 and the electronic device 110 may implement signaling interaction through the communication connection between the two.

[0032] It should be understood that the structure and function of the various elements in the environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of the present disclosure.

[0033] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0034] Example Process

[0035] FIG2 shows a flow chart of an example information processing process 200 according to some embodiments of the present disclosure. The process 200 may be implemented at the electronic device 110. The process 200 is described below with reference to FIG1.

[0036] As shown in FIG. 2 , in block 210 , the electronic device 110 obtains target response content of a target model for a target query term, where the target response content indicates a group of objects determined based on the target query term.

[0037] In some embodiments, the target model may be a language model. In addition, the target model may also be an agent or a bot with search recommendation capabilities. As an example, the agent or bot may process a target query item (query) input by the user based on configured knowledge information, plug-in information, system prompt words, etc. In some embodiments, the target query item may correspond to a search request for points of interest, such points of interest including but not limited to coffee shops, restaurants, and stores. As an example, such a target query item may be, for example, "coffee shops with good surroundings."

[0038] Furthermore, the target model may determine corresponding target response content based on the user's target query. In some embodiments, the target response content may be a set of objects determined by the target model based on the target query, and such a set of objects may be multiple points of interest determined by the target model based on the point of interest search request.

[0039] As an example, the target model can determine a corresponding set of coffee shops (or objects) that meet the conditions based on the query item "coffee shops with good surroundings" entered by the user. Such a set of objects can include multiple points of interest such as object 1, object 2, and object 3.

[0040] In this way, the electronic device 110 can obtain target response content for the target query item based on the target model. In the target response content, multiple points of interest can be displayed in a predetermined order, for example, to indicate the order of the environment.

[0041] For ease of understanding, the following describes the processing process for the target query item.

[0042] In block 220 , the electronic device 110 determines at least one query constraint based on the target query term.

[0043] In some embodiments, the query constraints may include entity constraints and / or condition constraints. For example, such entities may be coffee shops, stores, and schools, and the conditions may be good environment, nearby, and well-received.

[0044] In some embodiments, the electronic device 110 may determine entity constraints and / or conditional constraints associated with the object to be searched based on the target query term.

[0045] In some embodiments, the electronic device 110 may identify keywords associated with entity constraints and conditional constraints in the target query based on a preset method. In some embodiments, such a preset method may, for example, involve the electronic device 110 extracting keywords from the target query based on a recognition model and determining query constraints based on the keywords. Alternatively, the electronic device 110 may also utilize a language model to process the target query to output the entity constraints and / or conditional constraints included in the target query.

[0046] In some examples, an entity constraint may indicate a specific point of interest associated with the object to be searched, and a conditional constraint may indicate characteristics of the point of interest associated with the object to be searched. For example, in the target query "coffee shop with good environment", the entity constraint may be "coffee shop" and the conditional constraint may be "good environment".

[0047] In this way, the electronic device 110 can determine entity constraints and / or condition constraints based on the target query term, so that feedback on the target corresponding content can be determined based on a combination of the query constraints and the target response content.

[0048] At block 230 , the electronic device 110 generates a set of additional query terms based on the target response content and at least one query constraint.

[0049] In some embodiments, the electronic device 110 may construct a set of initial query terms based on the target response content and the query constraints.

[0050] As an example, the electronic device 110 may construct a set of initial query items based on Object 1, Object 2, and Object 3, as well as “coffee shop” and “good environment”.

[0051] For the convenience of description, the initial query items corresponding to the entity constraints may be referred to as the first group of initial query items, and the initial query items corresponding to the condition constraints may be referred to as the second group of initial query items.

[0052] For example, the first set of initial query items may be, for example, “Is object 1 a coffee shop?”, “Is object 2 a coffee shop?”, and “Is object 3 a coffee shop?” The second set of initial query items may be, for example, “Is object 1 a good environment?”, “Is object 2 a good environment?”, and “Is object 3 a good environment?”

[0053] In some embodiments, such a set of initial query terms may include both entity constraints and condition constraints, or may include only one of these constraints. Furthermore, it is understood that such a set of initial query terms may also include other types of query constraints. This disclosure is not intended to limit the number of query constraints.

[0054] Furthermore, the electronic device 110 may send such a set of initial query terms to the target model to instruct the target model to generate a set of additional query terms based on the set of initial query terms.

[0055] In some embodiments, such a target model may be a similar semantic rewriting model. In some embodiments, electronic device 110 may rewrite a set of initial query items into a set of additional query items with the same semantics but different expressions based on the similar semantic rewriting model. For example, electronic device 110 may rewrite "Is object 1 a coffee shop" into "Does object 1 make coffee?"

[0056] In some embodiments, such a set of additional query items may include a first query item and a second query item. In some embodiments, the rewritten initial query item corresponding to the entity constraint may be referred to as the first query item, and the rewritten initial query item corresponding to the condition constraint may be referred to as the second query item.

[0057] For example, the first query item could be "Is object 1 a coffee shop?", "Is object 2 a coffee shop?", and "Is object 3 appropriately categorized as a coffee shop?" The second query item could be "How is the environment of object 1?", "Is the environment of object 2 bad?", and "What do you think of the environment of object 3?"

[0058] In some embodiments, the first query item can be used to verify whether at least one object in a group of objects satisfies an entity constraint. For example, electronic device 110 can query the first query item "Does object 1 make coffee?" based on the target model to obtain corresponding response content, thereby verifying whether object 1 satisfies the entity constraint, that is, whether it is a coffee shop.

[0059] Similar to the above, in some embodiments, the second query item can be used to verify whether at least one object in a group of objects satisfies a conditional constraint. For example, electronic device 110 can query the second query item "How is the environment of object 1" based on the target model to obtain corresponding response content, thereby verifying whether object 1 satisfies the conditional constraint, that is, whether the environment is good.

[0060] In some embodiments, the conditional constraints may further include ranking constraints related to the ranking conditions. For example, such ranking constraints may include nearest, best environment, and so on. In other words, such a set of additional query items may further include a third query item. In some embodiments, the third query item may be used to verify whether the ranking of multiple objects in a set of objects in the target response content satisfies the ranking constraints.

[0061] As an example, the electronic device 110 may query the third query item “Is the environment of object 1 better than that of object 2” based on the target model to obtain corresponding response content, thereby verifying whether objects 1 and 2 meet the sorting constraint.

[0062] At block 240 , the electronic device 110 determines evaluations for target response content based on a set of additional response content generated by the target model for the set of additional query terms.

[0063] For ease of description, the following description will be made by taking the first query item as an example.

[0064] In some embodiments, the electronic device 110 may provide the target query term, target response content, additional query terms, and additional response content corresponding to the additional query terms to the target model to instruct the target model to determine whether the target response content matches the additional response content.

[0065] Furthermore, the electronic device 110 may determine an evaluation of the target response content based on the processing result of the target model. In some embodiments, such an evaluation at least indicates whether the description of the target response content for the corresponding object matches the corresponding additional response content.

[0066] For example, the target query item is "coffee shops with good environment," and the target response content determined by the target model is "Object 1 is a coffee shop with good environment." An additional query item may be "Is Object 1 a coffee shop?" Furthermore, electronic device 110 may determine additional response content corresponding to the additional query item based on the target model. For example, such additional response content may be "Object 1 is a coffee shop" or "Object 1 is not a coffee shop."

[0067] In some embodiments, if the electronic device 110 responds with the additional response content "Object 1 is a coffee shop," it indicates that the target model determines that the target response content matches the additional response content. In some embodiments, if the electronic device 110 responds with the additional response content "Object 1 is not a coffee shop," it indicates that the target model determines that the target response content does not match the additional response content.

[0068] Further, in some embodiments, the electronic device 110 may generate feedback information for the target query item in response to an evaluation indicating that the target response content's description of the corresponding object does not match the corresponding additional response content, to indicate that the target response content generated by the target model has failed the test.

[0069] Figure 3 illustrates an example process for information processing according to some embodiments of the present disclosure. As shown in Figure 3 , the target query item is "coffee shops with the best surroundings" 310, and the target model's target response content 311 for the target query item is "Object 1," "Object 2," and "Object 3." Furthermore, electronic device 110 may obtain entity constraints 312 and conditional constraints 313 for the target query item and construct a set of initial query items based on the target response content 311 and at least one query constraint.

[0070] The electronic device 110 may rewrite such a set of initial query items into a set of additional query items 315 based on the similar semantic rewriting model 314. Furthermore, the electronic device 110 may obtain a set of additional response content 316 corresponding to the set of additional query items 315 based on the target model.

[0071] The electronic device 110 may determine whether the target response content 311 and a set of additional response content 316 match 317. If not, the electronic device 110 may generate feedback information 318 for the target query item to indicate that the target response content generated by the target model fails the test.

[0072] Furthermore, in some embodiments, the electronic device 110 may also determine whether the conditional constraints 313 include a ranking requirement 319. If so, a ranking verification may be required. Specifically, the electronic device 110 may generate a ranking verification query item 320 based on the target model and determine corresponding response content 321. Furthermore, the electronic device 110 may determine whether the target response content 311, the set of additional response content 316, and the response content 321 match. If not, the electronic device 110 may generate feedback information 318 for the target query item to indicate that the target response content generated by the target model has failed the test.

[0073] In this way, the embodiments of the present disclosure can support users to evaluate the target response content of the target query item through the model, thereby more efficiently screening out the target response content that fails the test and improving the accuracy of information processing.

[0074] Example devices and equipment

[0075] Embodiments of the present disclosure also provide corresponding apparatuses for implementing the above-described methods or processes. FIG4 shows a schematic block diagram of an example apparatus 400 for information processing according to certain embodiments of the present disclosure. Apparatus 400 may be implemented as or included in electronic device 110. Each module / component in apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0076] As shown in Figure 4, the device 400 includes a content acquisition module 410, which is configured to obtain target response content of the target model for the target query item, the target response content indicating a group of objects determined based on the target query item; a constraint determination module 420, which is configured to determine at least one query constraint based on the target query item; a content generation module 430, which is configured to generate a group of additional query items based on the target response content and at least one query constraint; and an evaluation determination module 440, which is configured to determine an evaluation of the target response content based on a group of additional response content generated by the target model for the group of additional query items.

[0077] In some embodiments, the constraint determination module 420 is further configured to determine entity constraints associated with the object to be searched based on the target query term; and / or determine conditional constraints associated with the object to be searched based on the target query term.

[0078] In some embodiments, the set of additional query items includes: a first query item for verifying whether at least one object in a set of objects satisfies an entity constraint; and / or a second query item for verifying whether at least one object in a set of objects satisfies a conditional constraint.

[0079] In some embodiments, the conditional constraint includes a sorting constraint related to the sorting condition, and the set of additional query items includes: a third query item for verifying whether the sorting of multiple objects in the set of objects in the target response content satisfies the sorting constraint.

[0080] In some embodiments, the content generation module 430 is further configured to construct a set of initial query terms based on the target response content and at least one query constraint; and send the set of initial query terms to the target model to instruct the target model to generate a set of additional query terms based on the set of initial query terms.

[0081] In some embodiments, the evaluation indicates at least whether the description of the corresponding object by the target response content matches the corresponding additional response content.

[0082] In some embodiments, the evaluation determination module 440 is further configured to provide the target query item, target response content, additional query items, and additional response content corresponding to the additional query items to the target model to instruct the target model to determine whether the target response content matches the additional response content; and determine an evaluation of the target response content based on the processing results of the target model.

[0083] In some embodiments, the device 400 also includes an information generation module, which is configured to generate feedback information for the target query item in response to an evaluation indicating that the target response content's description of the corresponding object does not match the corresponding additional response content, to indicate that the target response content generated by the target model has failed the test.

[0084] In some embodiments, the target query term corresponds to a point of interest search request, and the set of objects includes a plurality of points of interest determined by the target model based on the point of interest search request.

[0085] The modules included in the device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units can be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the modules in the device 400 can be implemented at least in part by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0086] As shown in FIG5 , electronic device 500 is a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors or processing units 510, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processing unit 510 may be a real or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to enhance the parallel processing capabilities of electronic device 500.

[0087] The electronic device 500 typically includes a plurality of computer storage media. Such media can be any accessible media that can be obtained by the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk, or any other medium that can be used to store information and / or data and can be accessed within the electronic device 500.

[0088] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 5 , a disk drive for reading from or writing to a removable, non-volatile disk (e.g., a “floppy disk”) and an optical drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 520 may include a computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

[0089] The communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0090] Input device 550 may be one or more input devices, such as a mouse, keyboard, or trackball. Output device 560 may be one or more output devices, such as a display, a speaker, or a printer. Electronic device 500 may also communicate with one or more external devices (not shown) via communication unit 540 as needed, such as a storage device, a display device, or the like, with one or more devices that allow a user to interact with electronic device 500, or with any device that allows electronic device 500 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0091] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0092] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0093] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0094] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0095] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0096] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, not exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for information processing, comprising: Obtaining target response content of a target model for a target query item, wherein the target response content indicates a group of objects determined based on the target query item; Determining at least one query constraint based on the target query term; generating a set of additional query terms based on the target response content and the at least one query constraint; as well as An evaluation for the target response content is determined based on a set of additional response content generated by the target model for the set of additional query terms.

2. The method according to claim 1, wherein determining at least one query constraint based on the target query term comprises: Determining entity constraints associated with the object to be searched based on the target query term; and / or Based on the target query term, conditional constraints associated with the object to be searched are determined.

3. The method of claim 2, wherein the set of additional query terms comprises: a first query item, for verifying whether at least one object in the set of objects satisfies the entity constraint; and / or The second query item is used to verify whether at least one object in the group of objects satisfies the conditional constraint.

4. The method of claim 2, wherein the conditional constraint comprises a sorting constraint related to a sorting condition, and the set of additional query terms comprises: The third query item is used to verify whether the order of multiple objects in the group of objects in the target response content satisfies the order constraint.

5. The method of claim 1 , wherein generating a set of additional query terms based on the target response content and the at least one query constraint comprises: constructing a set of initial query terms based on the target response content and the at least one query constraint; as well as The initial set of query terms is sent to the target model to instruct the target model to generate the set of additional query terms based on the initial set of query terms. The method according to claim 1 , wherein the evaluation at least indicates whether the description of the corresponding object in the target response content matches the corresponding additional response content.

7. The method of claim 6, wherein determining an evaluation of the target response content based on a set of additional response content generated by the target model for the set of additional query terms comprises: providing the target query term, the target response content, an additional query term, and an additional response content corresponding to the additional query term to the target model, so as to instruct the target model to determine whether the target response content matches the additional response content; as well as The evaluation of the target response content is determined based on the processing result of the target model.

8. The method according to claim 6, further comprising: In response to the evaluation indicating that the description of the corresponding object in the target response content does not match the corresponding additional response content, feedback information is generated for the target query item to indicate that the target response content generated by the target model fails the test. 9 . The method of claim 1 , wherein the target query term corresponds to a point of interest search request, and the set of objects includes a plurality of points of interest determined by the target model based on the point of interest search request.

10. An apparatus for information processing, comprising: a content acquisition module configured to acquire target response content of a target model for a target query item, wherein the target response content indicates a group of objects determined based on the target query item; A constraint determination module, configured to determine at least one query constraint based on the target query item; a content generation module configured to generate a set of additional query terms based on the target response content and the at least one query constraint; as well as The evaluation determination module is configured to determine an evaluation for the target response content based on a set of additional response content generated by the target model for the set of additional query items.

11. An electronic device comprising: at least one processing unit; as well as At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 9 when executed by the at least one processing unit.

12. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 9.

13. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 9.

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