Method, device and storage medium for generating research report based on generative large model

Abstract

The present disclosure provides a method, device and storage medium for generating a research report based on a generative large model, which relates to technical fields such as artificial intelligence. The specific implementation scheme is: based on the input information obtained from the user, the title and description information of the research report are obtained; the description information is used to limit the research report to be generated; based on the title and the description information, knowledge information is obtained; based on the title and the description information, a pre-trained generative large model is used to generate a target outline of the research report using at least two steps; the target outline includes multiple chapter titles with a sequential relationship and the target number of words of each chapter content; based on the target outline and the knowledge information, the generative large model is used to generate the main content of the research report. The technology disclosed in the present disclosure can effectively improve the generation efficiency of research reports.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to technical fields such as artificial intelligence, and more particularly to a method, device, and storage medium for generating a research report based on a generative large model. Background Art

[0002] The research report is mainly completed through a large amount of preliminary research on the corresponding subject area, statistical analysis of the data, and finally an integration of a highly professional report.

[0003] In the existing technology, considering the professionalism of research reports, professional practitioners in the field usually use their understanding of the field and analysis of a large amount of factual information and data in the field to screen out highly relevant information based on the report topic, conduct statistical mapping, in-depth analysis, etc., and finally write a complete report. Summary of the Invention

[0004] The present disclosure provides a method, device, and storage medium for generating a research report based on a generative large model.

[0005] According to one aspect of the present disclosure, a method for generating a research report based on a generative large model is provided, comprising:

[0006] Based on the user input information obtained, obtain the title and description information of the research report; the description information is used to define the research report to be generated;

[0007] Acquiring knowledge information based on the title and the description information;

[0008] Based on the title and the description information, a pre-trained generative model is used to generate a target outline of the research report in at least two steps; the target outline includes a plurality of chapter titles in a sequential relationship and a target word count for each chapter content;

[0009] Based on the target outline and the knowledge information, the generative model is used to generate the main content of the research report.

[0010] According to another aspect of the present disclosure, there is provided an apparatus for generating a research report based on a generative large model, comprising:

[0011] An information acquisition module, configured to acquire a title and description information of a research report based on the input information obtained from the user; the description information is used to define the research report to be generated;

[0012] A knowledge acquisition module, configured to acquire knowledge information based on the title and the description information;

[0013] An outline generation module is configured to generate a target outline for the research report based on the title and the description information using a pre-trained generative model and at least a two-step generation method; the target outline includes a plurality of sequential chapter titles and a target word count for each chapter content;

[0014] A content generation module is used to generate the main content of the research report based on the target outline and the knowledge information using the generative model.

[0015] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0019] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the above-mentioned aspect and any possible implementation manner.

[0020] According to yet another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the above-mentioned aspects and any possible implementation method when executed by a processor.

[0021] According to the technology disclosed herein, the efficiency of generating research reports can be effectively improved. It should be understood that the content described in this section is not intended to identify the key 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 easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0023] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0027] Figure 5 is a block diagram of an electronic device for implementing the method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0030] It should be noted that the terminal devices involved in the embodiments of the present disclosure may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0031] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0032] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure; Figure 1 As shown, this embodiment provides a method for generating a research report based on a generative large model, which may specifically include the following steps:

[0033] S101. Based on the user input information obtained, obtain the title and description information of the research report; the description information is used to define the research report to be generated;

[0034] In this embodiment, the user's input information may be text information, or may also be voice information, and the text information may be obtained by using voice recognition technology.

[0035] The descriptive information in this embodiment can be understood as various limiting information for the research report that the user desires to generate. For example, the descriptive information may include the content that the user desires to include in the research report. For example, the descriptive information may be in the form of a string and / or a statement to define the content that needs to be included in the research report. Specifically, the descriptive information may include one, two, or more strings, or one, two, or more statements. Alternatively, the descriptive information may be considered to include conditional information that needs to be referenced when generating the research report.

[0036] In one application scenario of this embodiment, the user's input information may be: "Help me generate a research report on field B in field A, which may include analysis of knowledge points C and D." The title of the research report obtained in this case may be: "Research on field B in field A," and the description information may include knowledge points C and D. For another example, the user's input information may also include the expected word count of the research report, the expected outline structure, and so on. This information can serve as descriptive information to define the research report, so that the descriptive information can be referenced later to generate a research report that meets the user's needs.

[0037] In this embodiment, the process of obtaining the title and description information of the research report from the user's input information can be implemented by using information mining technology.

[0038] In actual applications, the user's input information may also carry other descriptive information of the research report, which will not be listed here one by one.

[0039] S102. Acquire knowledge information based on the title and description information;

[0040] This step S102 can be understood as a knowledge information retrieval process. When generating a research report, it is far from enough to rely solely on the title and description information. It is necessary to obtain knowledge information related to the title and description information to provide rich data support for the subsequent generation of the research report.

[0041] S103. Based on the title and description information, a pre-trained generative model is used to generate a target outline for the research report using at least a two-step generation method; the target outline includes multiple chapter titles in a sequential relationship and a target word count for each chapter content;

[0042] The target outline of the research report of this embodiment is not generated in one go, but rather is generated in at least two steps. This enriches the structure of the target outline of the research report, making it more accurate and reasonable. Furthermore, the target outline of the research report generated by this embodiment can also include a target word count for each chapter. The target word count can be lower for less important chapters, while it can be higher for more important chapters, providing accurate guidance for the generation of subsequent chapter content.

[0043] S104. Based on the target outline and knowledge information, a generative big model is used to generate the main content of the research report.

[0044] Specifically, the generative model uses the target outline and references knowledge information to generate the content of each chapter in the target outline, thereby obtaining the main body of the research report. The content of each chapter is generated based on the target word count of the corresponding chapter in the target outline. In other words, the actual word count of each generated chapter should theoretically be greater than or equal to the target word count of the corresponding chapter in the target outline.

[0045] In this embodiment, the generative large model used can also be called a generative language model (General Language Model; GLM), or a generative large language model.

[0046] The method of generating a research report based on a generative large model in this embodiment can intelligently and automatically generate a research report by adopting the above-mentioned scheme. Compared with the manual generation of the existing technology, it can not only effectively shorten the time consumption of the research report and effectively improve the generation efficiency of the research report; but also effectively ensure the accuracy of the generated research report.

[0047] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure; the method of generating a research report based on a generative large model in this embodiment, in the above Figure 1 Based on the technical solutions of the embodiments shown, the technical solutions of the present disclosure are further described in more detail. Figure 2 As shown, the method for generating a research report based on a generative large model in this embodiment may specifically include the following steps:

[0048] S201. Based on the user input information obtained, a pre-trained information acquisition model is used to obtain the title and description information of the research report; the description information is used to define the research report to be generated;

[0049] In this embodiment, the pre-trained information acquisition model may be a base class large model that can acquire effective information from the user's input information and obtain the title and description information of the research report.

[0050] S202, based on the title and description information, using a professional information acquisition module to acquire professional knowledge from a pre-created professional knowledge database;

[0051] S203, based on the title and description information, using a real-time information acquisition module to acquire real-time information knowledge through an external search engine and a third-party data source;

[0052] In this embodiment, for each field, a professional knowledge base of the field may be created in advance, which may include all the professional knowledge of the field.

[0053] That is to say, when this embodiment is applied, the field to which the research report belongs may be known in advance, and the professional knowledge base corresponding to the field may also be created in advance.

[0054] The professional information acquisition module can search in the professional knowledge base of the field to which the research report belongs in advance based on the title and description information to obtain professional knowledge. During the specific search, the title and description information can be segmented at multiple granularities first, and then each word obtained by the segmentation can be searched in the professional knowledge base at the granularity to obtain all the professional knowledge related to the words. For example, the professional knowledge related to each word can include the explanation of the word in the field to which the research report belongs, and can also include the application scenarios and application examples of the word in the field, etc. The multi-granularity word segmentation of this embodiment can refer to word segmentation of all granularities. For example, for the string XYZ, the multi-granularity word segmentation of this embodiment can include X, Y, and Z word segmentation, can also include word segmentation of XY and YZ granularity, and can also include word segmentation of XYZ granularity.

[0055] The real-time information acquisition module can obtain real-time information knowledge from the public network and third-party data sources through an external search engine. The method of obtaining this real-time information knowledge can be the same as the method of obtaining professional knowledge, or it can be obtained by retrieving the knowledge related to each word after multi-granularity word segmentation. For example, real-time information knowledge can include at least one aspect of information such as the explanation, application scenario, and application example of each word in the general field of the public network, as well as all relevant data information of each word in the third-party data source. Compared with professional knowledge, real-time information knowledge is not limited to the field to which the research report belongs, and can effectively supplement professional knowledge, making the knowledge referenced when generating the research report richer and more comprehensive.

[0056] In this embodiment, steps S202-S203 are as described above. Figure 1An implementation of step S102 of the illustrated embodiment. The professional knowledge acquired in step S202 can effectively ensure the professionalism of the research report, and the real-time information knowledge acquired in step S203 can effectively ensure the real-time nature of the research report, and can also enable the research report to be integrated with general field knowledge, making it more comprehensive. Optionally, step S203 can also be deleted, and only step S202 is included. Of course, when step S203 is included, the acquired knowledge and information content is more comprehensive and rich, which can effectively ensure that the content of the generated research report is more accurate and reasonable.

[0057] S204. Based on the title and description information, use the generative model to generate a preliminary outline of the research report;

[0058] The primary outline of the research report of this embodiment can be considered as an outline with a relatively simple structure. Compared with the target outline, the primary outline does not include enough architectural levels.

[0059] S205. Based on the primary outline, title, and description information of the report, a generative model is used to generate a structure of a target outline of the research report in at least one step. The structure of the target outline of the research report includes a plurality of sequentially related section titles.

[0060] Specifically, the generative model can be based on the primary outline, further refer to the title and description information, enrich the structure of the primary outline, and generate the target outline.

[0061] Optionally, in this embodiment, a two-step generation method can be used to generate the structure of the target outline. Alternatively, a multi-step, cyclical generation method can be used, continuously utilizing the outline generated in the previous step, and referring to the title and description information, using a generative model to further enrich the generated outline until the target outline of the research report is generated. The specific number of steps required for the outline generation process can be pre-set. In short, the more generation steps are used in the process of generating the target outline, the richer the structure of the generated target outline.

[0062] S206, using a generative large model to predict the target word count of each chapter in the target outline;

[0063] In this embodiment, the generative model can directly predict the target number of words included in the content of each chapter in the target outline.

[0064] Alternatively, in one embodiment of the present disclosure, if the description information includes the expected word count of the research report, step S206 may further include the following steps:

[0065] (1) Based on the descriptive information, obtain the expected word count of the research report;

[0066] This step can also be implemented using a pre-trained information acquisition model.

[0067] (2) Based on the expected word count of the research report, a generative large model is used to predict the target word count of each chapter in the target outline.

[0068] When predicting the target word count for each chapter in the target outline, the generative model can reference the expected word count for the research report. For example, the target word count for more important chapters in the target outline can be predicted to be higher, while the target word count for less important chapters can be predicted to be lower. In short, the sum of the target word counts for all chapters should theoretically be greater than or equal to the expected word count for the research report. This effectively ensures that the target word count for each chapter is predicted to be more accurate.

[0069] In this embodiment, steps S204-S206 are as described above. Figure 1 An implementation of step S103 of the illustrated embodiment.

[0070] S207. Based on the target outline, professional knowledge and real-time information knowledge, a generative large model is used to generate the main content of the research report;

[0071] Specifically, the generative big model can generate the content for each chapter in the target outline based on professional knowledge and real-time information, thereby generating the main body of the research report. This means that the title and main body of the research report are already generated. If additional content is not needed, the research report title, target outline, and main body can be integrated to generate the research report.

[0072] Optionally, in an embodiment of the present disclosure, when implementing step S207, the following steps may be specifically adopted:

[0073] (a) Based on professional knowledge, real-time information knowledge, and the chapter titles in the target outline, a generative large model is used to generate the content of each chapter;

[0074] That is to say, for each chapter, the generative big model can generate the content of the chapter based on professional knowledge, real-time information knowledge and the title of the chapter.

[0075] (b) For any chapter, if the word count of the chapter content is less than the corresponding target word count, based on professional knowledge, real-time information knowledge, and the generated chapter content and the chapter title corresponding to the chapter, a generative large model is used to expand the chapter content until the word count of the chapter content is equal to or greater than the corresponding target word count.

[0076] In this embodiment, the step of expanding the chapter content may include only one round of expansion, or two rounds of expansion or multiple rounds of expansion. After each round of expansion, it is necessary to detect whether the number of words in the content of the expanded chapter is equal to or greater than the corresponding target number of words. If so, the process ends; otherwise, the expansion continues.

[0077] It should be noted that the generation of the content of each chapter can be independent or can be generated by influencing each other, so that the context of the research report is more coherent. For example, the content of each chapter can be generated in sequence according to the position order of each chapter from front to back in the outline. When the content of other chapters other than the first chapter is generated, the content of the first preset number of chapters before the current chapter can be used as context information and input into the generative model together. The generative model will refer to the context information at the same time to generate the content of the current chapter, so that the context content of the generated research report is more coherent. The first preset number can be set according to actual needs or experience, for example, it can be 1, 2 or more, and is not limited here.

[0078] In the present embodiment, in the process of specifically generating the contents of each chapter in the research report, in the first round of generation, the contents of each chapter can be generated in sequence according to the above-mentioned method. Afterwards, it is detected whether the number of words in the contents of each chapter reaches the target number of words corresponding to the chapter specified in the target outline. If not reached, then based on professional knowledge, real-time information knowledge, and the content of the generated chapter and the chapter title corresponding to the chapter, the generative large model is used to expand the content of the chapter. It should be noted that when expanding the content of a chapter, the contents of a second preset number of chapters before and after the current chapter can be used as context information and input into the generative large model together. The generative large model simultaneously refers to the context information to expand the content of the current chapter, so that the context content of the generated research report is more coherent.

[0079] Through the above method, it can be ensured that the content of each chapter in the generated research report is rich enough.

[0080] S208. Based on the content of the report body, use a generative macro model to generate at least one of a summary, a statement, an appendix, and a reference of the research report;

[0081] It should be noted that, in general, a research report needs to include each of the abstract, statement, appendix, and references. In specific implementation, the information content of each of them can also be obtained according to step S208. However, in actual applications, at least one of the above information can also be obtained according to specific needs.

[0082] S209. Generate a research report based on the research report's abstract, statement, appendix, and at least one of the references, as well as the research report's title, objective outline, and main body of the research report.

[0083] Specifically, the order of each part of information when integrating and generating a research report can refer to the order of each part of information in a manually written research report, or the order of each part can be limited according to needs, which is not limited here.

[0084] The method of generating a research report based on a generative large model in this embodiment can generate a target outline of the research report through at least two steps, which can effectively ensure that the structure of the generated target outline is richer and more accurate.

[0085] Moreover, in this embodiment, the expected number of words in the research report can also be obtained, and based on the expected number of words in the research report, a generative large model can be used to accurately predict the target number of words included in the content of each chapter in the target outline, thereby further effectively enriching the content of the target outline.

[0086] Furthermore, in this embodiment, the knowledge information acquired based on the title and description information includes not only professional knowledge but also real-time information knowledge, thereby providing effective data support for the generation of research report content.

[0087] Furthermore, in this embodiment, the content of each chapter in the research report may be generated through multiple expansions, which can effectively ensure the richness of the content of the generated research report.

[0088] In summary, by adopting the above solution, this embodiment can generate professional research reports in related fields quickly, efficiently and with high quality.

[0089] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure; Figure 3 As shown, this embodiment provides an apparatus 300 for generating a research report based on a generative large model, comprising:

[0090] The information acquisition module 301 is used to acquire the title and description information of the research report based on the user input information; the description information is used to define the research report to be generated;

[0091] A knowledge acquisition module 302 is configured to acquire knowledge information based on the title and the description information;

[0092] An outline generation module 303 is configured to generate a target outline for the research report based on the title and the description information using a pre-trained generative model in at least two steps; the target outline includes a plurality of sequential chapter titles and a target word count for each chapter;

[0093] The content generation module 304 is used to generate the main content of the research report based on the target outline and the knowledge information using the generative macro model.

[0094] The device 300 for generating a research report based on a generative large model in this embodiment realizes the implementation principle and technical effect of generating a research report based on a generative large model by adopting the above-mentioned modules, which is the same as the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, which will not be repeated here.

[0095] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure; Figure 4 As shown, the apparatus 400 for generating a research report based on a generative large model in this embodiment includes Figure 3 The apparatus 300 for generating a research report based on a generative large model has modules with the same names and functions as: information acquisition module 401 , knowledge acquisition module 402 , outline generation module 403 and content generation module 404 .

[0096] like Figure 4 As shown, the outline generation module 403 includes:

[0097] A first outline generating unit 4031 is configured to generate a primary outline of the research report based on the title and the description information using the generative model;

[0098] A second outline generating unit 4032 is configured to generate a target outline structure of the research report based on the primary outline of the research report, the title, and the description information using the generative model and at least one step of generation; the target outline structure of the research report includes a plurality of sequentially related chapters.

[0099] The prediction unit 4033 is used to use the generative large model to predict the target number of words included in the content of each chapter in the target outline.

[0100] Further optionally, the prediction unit 4033 is configured to:

[0101] Based on the description information, obtaining an expected word count of the research report;

[0102] Based on the expected word count of the research report, the generative model is used to predict the target word count of each chapter in the target outline.

[0103] Further optionally, as Figure 4 As shown, the knowledge acquisition module 402 includes:

[0104] The professional knowledge acquisition unit 4021 is configured to acquire professional knowledge from a pre-created professional knowledge base using a professional information acquisition module based on the title and the description information;

[0105] The real-time knowledge acquisition unit 4022 is configured to acquire real-time information knowledge from the public network and third-party data sources based on the title and the description information by using a real-time information acquisition module and an external search engine.

[0106] Further optionally, a content generation module 404 is configured to generate the main content of the research report based on the target outline, the professional knowledge and the real-time information knowledge using the generative macro model;

[0107] Further optionally, as Figure 4 As shown, the apparatus 400 for generating a research report based on a generative large model in this embodiment further includes:

[0108] The integration module 405 is configured to integrate and generate the research report based on the title of the research report, the objective outline, and the main content of the research report.

[0109] Further optionally, as Figure 4 As shown, in the apparatus 400 for generating a research report based on a generative large model in this embodiment, the content generation module 404 includes:

[0110] A content generation unit 4041 is configured to generate the content of each chapter using the generative model based on the professional knowledge, the real-time information knowledge, and the chapter titles in the target outline;

[0111] The content expansion unit 4042 is used to expand the content of any chapter, if the number of words in the content of the chapter is less than the corresponding target number of words, based on the professional knowledge, the real-time information knowledge, and the generated content of the chapter and the chapter title corresponding to the chapter, using the generative large model to expand the content of the chapter until the number of words in the content of the chapter is equal to or greater than the corresponding target number of words.

[0112] Further optionally, the content generating unit 4041 is further configured to:

[0113] Based on the content of the report body, using the generative macro model, generate at least one of the summary, statement, appendix, and reference of the research report;

[0114] The integration module 405 is configured to integrate and generate the research report based on at least one of the abstract, statement, appendix, and references of the research report, as well as the title of the research report, the objective outline, and the main body of the research report.

[0115] The device 300 for generating a research report based on a generative large model in this embodiment realizes the implementation principle and technical effect of generating a research report based on a generative large model by adopting the above-mentioned modules, which is the same as the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, which will not be repeated here.

[0116] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0117] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0118] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0119] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the RAM 503. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0120] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The computing unit 501 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the above-mentioned methods of the present disclosure. For example, in some embodiments, the above-mentioned methods of the present disclosure can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the above-mentioned methods of the present disclosure described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the above-mentioned methods of the present disclosure by any other appropriate means (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0128] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0129] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for generating a research report based on a generative large model, comprising: Based on the input information obtained from the user, obtain the title and description information of the research report; The description information is used to define the research report to be generated; Acquiring knowledge information based on the title and the description information; Based on the title and the description information, a pre-trained generative model is used to generate a target outline of the research report in at least two steps; the target outline includes a plurality of chapter titles in a sequential relationship and a target word count for each chapter content; Based on the target outline and the knowledge information, the generative model is used to generate the main content of the research report.

2. The method according to claim 1, wherein Based on the title and the description information, a pre-trained generative model is used to generate a target outline of the research report in at least two steps, including: Based on the title and the description information, using the generative macro model, generate a primary outline of the research report; Based on the primary outline of the research report, the title, and the description information, the generative model is used to generate a structure of a target outline of the research report in at least one step; the structure of the target outline of the research report includes a plurality of chapter titles in a sequential relationship; The generative model is used to predict the target number of words included in the content of each chapter in the target outline.

3. The method according to claim 2, wherein: The generative model is used to predict the target word count of each chapter in the target outline, including: Based on the description information, obtaining an expected word count of the research report; Based on the expected word count of the research report, the generative model is used to predict the target word count of each chapter in the target outline.

4. The method according to claim 1, wherein Based on the title and the description information, knowledge information is acquired, including: Based on the title and the description information, a professional information acquisition module is used to acquire professional knowledge from a pre-created professional knowledge database; Based on the title and the description information, a real-time information acquisition module is used to acquire real-time information knowledge from the public network and third-party data sources through an external search engine.

5. The method according to claim 4, wherein Based on the target outline and the knowledge information, the generative model is used to generate the main content of the research report, including: Based on the target outline, the professional knowledge and the real-time information knowledge, the generative big model is used to generate the main content of the research report; Furthermore, the method further comprises: The research report is generated based on the title of the research report, the objective outline and the main content of the research report.

6. The method according to claim 5, wherein: Based on the target outline, the professional knowledge, and the real-time information knowledge, the generative big model is used to generate the main content of the research report, including: Based on the professional knowledge, the real-time information knowledge and the titles of each chapter in the target outline, the generative model is used to generate the content of each chapter; For any chapter, if the number of words in the content of the chapter is less than the corresponding target number of words, based on the professional knowledge, the real-time information knowledge, and the generated content of the chapter and the chapter title corresponding to the chapter, the generative big model is used to expand the content of the chapter until the number of words in the content of the chapter is equal to or greater than the corresponding target number of words.

7. The method according to any one of claims 1 to 6, wherein: The method further comprises: Based on the content of the report body, using the generative macro model, generate at least one of the summary, statement, appendix, and reference of the research report; The research report is generated by integrating at least one of the abstract, statement, appendix and reference of the research report, as well as the title of the research report, the objective outline and the main content of the research report.

8. A device for generating a research report based on a generative large model, comprising: The information acquisition module is used to obtain the title and description information of the research report based on the input information obtained from the user; The description information is used to define the research report to be generated; A knowledge acquisition module, configured to acquire knowledge information based on the title and the description information; An outline generation module is configured to generate a target outline for the research report based on the title and the description information using a pre-trained generative model and at least a two-step generation method; the target outline includes a plurality of sequential chapter titles and a target word count for each chapter content; A content generation module is used to generate the main content of the research report based on the target outline and the knowledge information using the generative model.

9. The device according to claim 8, wherein The outline generation module includes: A first outline generating unit, configured to generate a primary outline of the research report based on the title and the description information and using the generative model; A second outline generating unit is configured to generate a target outline structure of the research report based on the primary outline of the research report, the title, and the description information, using the generative model and at least one step of generation; the target outline structure of the research report includes a plurality of chapter titles in a sequential relationship; The prediction unit is used to use the generative large model to predict the target number of words included in the content of each chapter in the target outline.

10. The device according to claim 9, wherein The prediction unit is configured to: Based on the description information, obtaining an expected word count of the research report; Based on the expected word count of the research report, the generative model is used to predict the target word count of each chapter in the target outline.

11. The device according to claim 8, wherein The knowledge acquisition module includes: a professional knowledge acquisition unit, configured to acquire professional knowledge from a pre-created professional knowledge base using a professional information acquisition module based on the title and the description information; The real-time knowledge acquisition unit is used to acquire real-time information knowledge from the public network and third-party data sources based on the title and the description information by using a real-time information acquisition module and an external search engine.

12. The device according to claim 11, wherein The content generation module is configured to generate the main content of the research report based on the target outline, the professional knowledge, and the real-time information knowledge using the generative macro model; Furthermore, the device further comprises: An integration module is used to integrate and generate the research report based on the title of the research report, the target outline and the main content of the research report.

13. The device according to claim 12, wherein The content generation module includes: A content generation unit, configured to generate content for each chapter using the generative model based on the professional knowledge, the real-time information knowledge, and the chapter titles in the target outline; The content expansion unit is used to expand the content of any chapter, if the number of words in the content of the chapter is less than the corresponding target number of words, based on the professional knowledge, the real-time information knowledge, and the generated content of the chapter and the chapter title corresponding to the chapter, using the generative large model to expand the content of the chapter until the number of words in the content of the chapter is equal to or greater than the corresponding target number of words.

14. The device according to claim 13, wherein The content generation unit is further configured to: Based on the content of the report body, using the generative macro model, generate at least one of the summary, statement, appendix, and reference of the research report; The integration module is used to integrate and generate the research report based on at least one of the abstract, statement, appendix and reference of the research report, as well as the title of the research report, the objective outline and the main content of the research report.

15. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

17. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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