Long report generation method based on large model

Abstract

The invention provides a long report generation method based on a large model, and belongs to the technical field of natural language processing and artificial intelligence, and the method specifically comprises the following steps: analyzing a report demand input by a user, extracting key information, and generating task description; automatically generating a report framework based on the large model and the domain knowledge base, wherein the report framework comprises chapter division, title generation and content summary; calling a large model to generate detailed contents in chapters, and introducing a context memory mechanism to ensure the continuity between the chapters; grammar check, logic check and style optimization are performed on the generated content, and the professionality and accuracy of the report are improved in combination with a domain term library; and finally, outputting the report content according to a format required by a user, and automatically generating auxiliary contents such as a directory, a chart and reference literature. The method has the advantages of high efficiency, high quality, flexibility, expandability and the like, and can be widely applied to long report generation tasks in the fields of medical treatment, law, finance and the like.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing and artificial intelligence technology, and in particular to a method for generating long reports based on a large model. Background Art

[0002] With the rapid development of artificial intelligence technology, large models (such as GPT and BERT) have demonstrated powerful capabilities in natural language processing tasks. However, existing text generation technologies still have the following problems when processing long reports:

[0003] Insufficient content coherence: The generated text is prone to logical discontinuities or repetition.

[0004] Unclear structure: Long reports usually require clear chapter divisions and hierarchical structures. Existing technologies make it difficult to automatically generate a report framework that meets these requirements.

[0005] Redundant or missing information: The generated text may contain irrelevant information or omit key content.

[0006] Poor domain adaptability: For the generation of long reports in specific fields (such as medical, legal, and financial), existing technologies are difficult to meet the requirements of professionalism and accuracy.

[0007] Therefore, there is an urgent need for a method that can combine the capabilities of large models and automatically generate high-quality long reports. Summary of the Invention

[0008] In order to solve the above technical problems, the present invention provides a long report generation method based on a large model. By combining the text generation capability of the large model with structured processing technology, it solves the problems of poor coherence, unclear structure, redundant or missing information, etc. in the long report generation in the existing technology.

[0009] The technical solution of the present invention is:

[0010] A method for generating a long report based on a large model, comprising the following steps:

[0011] Input requirement analysis: Receive report requirements input by users and generate task descriptions through natural language understanding technology.

[0012] Report framework generation: Generate the basic framework of the report based on the big model and domain knowledge base, including chapter division, title generation and content summary.

[0013] Content generation and filling: Call the large model to generate detailed content by chapter, and introduce a contextual memory mechanism to ensure coherence between chapters.

[0014] Content optimization and polishing: Perform grammar checking, logic verification, and style optimization on the generated content, and enhance the professionalism of the report by combining it with the domain terminology library.

[0015] Output and format adjustment: Output the report content in the format required by the user, and automatically generate auxiliary content such as catalogue, charts and references.

[0016] Further,

[0017] The input requirements analysis step includes:

[0018] Receive user input on report topic, target audience, word count, field information, and formatting requirements;

[0019] Extract key information through natural language understanding technology and generate structured task descriptions.

[0020] The report framework generation step includes:

[0021] Call the large model to generate chapter division suggestions and content summary for the report;

[0022] Optimize the framework by combining it with a rule engine or machine learning model to ensure clear logic and distinct layers;

[0023] Personalize the framework based on user historical preferences or template data.

[0024] Use the rule engine to check the rationality of chapter division.

[0025] The frameworks are scored using a machine learning model to select the optimal framework solution.

[0026] The content generation and filling steps include:

[0027] The large model is called upon chapter by chapter to generate detailed content, and a context memory mechanism is introduced to avoid repetition or contradiction;

[0028] Integrate domain knowledge base to ensure the professionalism and accuracy of generated content;

[0029] The report content is gradually improved through multiple rounds of iterative mechanisms.

[0030] The content optimization and polishing steps include:

[0031] Use grammar checker to correct spelling, grammar, and punctuation errors;

[0032] Logic verification is performed through rule engines and machine learning models to ensure that the content is logical and consistent.

[0033] Optimize the style of report content based on domain terminology and user preferences.

[0034] Combine the domain terminology library and user preferences to optimize the style of the report content, including:

[0035] Use domain terminology to replace common vocabulary and improve the professionalism of reports;

[0036] Adjust the report's language style based on user preferences;

[0037] Generate more elegant expressions through large models to improve report readability.

[0038] The output and format adjustment steps include:

[0039] Convert report content into specified formats according to user needs, including Word, PDF, Markdown, etc.;

[0040] Automatically generate auxiliary content such as tables of contents, figures and references.

[0041] According to user needs, the report content is converted into a specified format, including:

[0042] Use document processing tools to convert formats.

[0043] During the export process, the report's chapter structure, heading styles, and paragraph formatting are preserved;

[0044] Automatically generate auxiliary content for reports, including:

[0045] Table of Contents: Automatically generate a table of contents based on chapter titles and support hyperlink jumps;

[0046] Charts: Automatically generate relevant charts based on the report content;

[0047] References: Automatically generate a reference list based on the cited content and support multiple citation formats.

[0048] The beneficial effects of the present invention are

[0049] 1. By combining the text generation capabilities of large models with structured processing technology, we achieve automated generation of report frameworks and content, significantly reducing report writing time. Traditional report writing requires a lot of manpower and material resources, while this invention can generate high-quality, long reports in just minutes.

[0050] 2. By introducing a contextual memory mechanism and multiple rounds of iterative optimization, the report content is ensured to be logically clear and coherent, avoiding common logical gaps or duplications in traditional technologies.

[0051] 3. With the continuous development of large model technology, this method can seamlessly integrate more advanced models to further improve the generation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] The present invention provides a method for generating a long report based on a large model, which specifically includes:

[0055] 1. Input demand analysis

[0056] Input requirements analysis is the first step in report generation. Its purpose is to clarify user needs and generate executable task descriptions. The specific steps are as follows:

[0057] 1.1 Receiving User Input

[0058] The user enters the report generation requirements through the interactive interface, including but not limited to the following:

[0059] Report topic: such as "Application of artificial intelligence in the medical field".

[0060] Target audience: such as medical industry practitioners, scientific researchers, etc.

[0061] Word count requirement: such as 10,000 words, 15,000 words, etc.

[0062] Field information: such as medical, financial, legal, etc.

[0063] Format requirements: such as Word, PDF, Markdown, etc.

[0064] Other special needs: such as whether charts, references, catalogs, etc. are needed.

[0065] 1.2 Requirements Analysis and Task Description Generation

[0066] Natural language understanding (NLU) technology is used to parse user input requirements, extract key information, and generate structured task descriptions. Specifically, it includes:

[0067] Use pre-trained language models (such as BERT, GPT, etc.) to perform semantic analysis on user input to identify key elements such as topic, audience, and word count.

[0068] Combined with the domain knowledge base, the system refines user needs. For example, if the user specifies the domain as "medical", the system will automatically load the terminology library and rule library in the medical field.

[0069] Generate a task description file as input for subsequent steps. The task description file includes the report topic, chapter division suggestions, keyword list, etc.

[0070] 2. Report framework generation

[0071] Generating a report framework is one of the core steps in generating a long report. Its purpose is to provide a clear structure and logical hierarchy for the report. The specific steps are as follows:

[0072] 2.1 Framework Generation Based on Large Model

[0073] The basic framework for calling large models (such as GPT-4) to generate reports. Specifically includes:

[0074] Generate chapter division suggestions for the report based on the topics and keywords in the task description. For example, for the topic "Application of Artificial Intelligence in the Medical Field", generate the following chapters:

[0075] Chapter 1: Overview of Artificial Intelligence in the Medical Field

[0076] Chapter 2: Application of Artificial Intelligence in Medical Image Analysis

[0077] Chapter 3: The Practice of Artificial Intelligence in Disease Diagnosis

[0078] Chapter 4: The Potential of Artificial Intelligence in Drug Discovery

[0079] Chapter 5: Future Development Trends and Challenges

[0080] Generate a brief summary of each chapter, outlining the core content of the chapter.

[0081] 2.2 Framework optimization and adjustment

[0082] Optimize the generated framework through rule engines or machine learning models to ensure its logic is clear and its structure is distinct. Specifically:

[0083] Use a rule engine to check the rationality of chapter divisions. For example, ensure that the content of each chapter is independent and complete, and avoid overlapping content between chapters.

[0084] The framework is personalized based on the user's historical preferences or template data. For example, if the user prefers to use "Introduction" instead of "Overview", the system will automatically adjust the section title.

[0085] The frameworks are scored using a machine learning model to select the optimal framework solution.

[0086] 3. Content generation and filling

[0087] After generating the report framework, the large model is called to generate detailed content by chapter, and the context memory mechanism is used to ensure the coherence between chapters. The specific steps are as follows:

[0088] 3.1 Chapter Content Generation

[0089] According to the report framework, the large model is called chapter by chapter to generate detailed content. Specifically including:

[0090] Generate one or more paragraphs of text for each chapter, ensuring that the content is consistent with the chapter outline.

[0091] During the generation process, a context memory mechanism is introduced to record the generated content to avoid duplication or contradiction.

[0092] Integrating domain knowledge bases ensures the professionalism and accuracy of generated content. For example, when generating the section "Application of Artificial Intelligence in Medical Image Analysis," the system automatically references relevant medical image analysis technologies and cases.

[0093] 3.2 Content consistency optimization

[0094] The following techniques are used to ensure coherence between chapters:

[0095] When generating each chapter, the system will refer to the content of the previous chapter to ensure a natural logical connection.

[0096] Use keyword extraction technology to identify the relevance between chapters and generate transition paragraphs through the large model.

[0097] Introducing a multi-round iteration mechanism to gradually improve the report content. For example, after generating the first draft, the system will regenerate and optimize the content to ensure overall coherence.

[0098] 4. Content optimization and polishing

[0099] After generating the first draft, optimize and polish the report content to improve its grammatical accuracy, logical clarity, and style consistency. The specific steps are as follows:

[0100] 4.1 Grammar Checking and Correction

[0101] Use a grammar checker like Grammarly to check your report and correct spelling, grammar, and punctuation errors.

[0102] 4.2 Logic Verification and Adjustment

[0103] Logical verification of report content is performed using rule engines and machine learning models to ensure that the content is logical and consistent with no contradictions. This includes:

[0104] Check the logical relationship between chapters to ensure that the content flows naturally.

[0105] Identify and correct logical errors in the content, such as inconsistencies, causal errors, etc.

[0106] 4.3 Style optimization and polishing

[0107] Combine domain terminology and user preferences to optimize the style of report content. Specifically including:

[0108] Use domain terminology to replace common vocabulary and enhance the professionalism of reports.

[0109] Adjust the report's language style based on user preferences, such as formal, concise, academic, etc.

[0110] Generate more elegant expressions through large models to improve report readability.

[0111] 4.4 Manual Feedback and Dynamic Adjustment

[0112] Users can provide feedback on generated content, and the system will dynamically adjust the generation strategy based on the feedback. For example, if a user is dissatisfied with the content of a certain chapter, the system will regenerate the content of that chapter.

[0113] 5. Output and format adjustment

[0114] After content optimization is completed, the report content will be output in the format required by the user, and auxiliary content such as the directory, charts, and references will be automatically generated. The specific steps are as follows:

[0115] 5.1 Format Conversion and Output

[0116] According to user needs, the report content is converted into a specified format (such as Word, PDF, Markdown, etc.). Specifically including:

[0117] Use a document processing tool (such as Pandoc) to convert the format.

[0118] During the export process, the report's chapter structure, heading styles, and paragraph formatting are preserved.

[0119] 5.2 Auxiliary Content Generation

[0120] Automatically generate auxiliary content for reports, including:

[0121] Table of Contents: Automatically generate a table of contents based on chapter titles and support hyperlink jumps.

[0122] Charts: Automatically generate relevant charts such as charts, flow charts, etc. based on the report content.

[0123] References: Automatically generate a reference list based on the cited content and support multiple citation formats (such as APA, MLA, etc.).

[0124] The above description is only a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for generating a long report based on a large model, characterized in that: The following steps are involved: Input requirements analysis: Receive report requirements input by users and generate task descriptions through natural language understanding technology; Report framework generation: Generate the basic framework of the report based on the big model and domain knowledge base, including chapter division, title generation and content summary; Content generation and filling: Calling the large model to generate detailed content by chapter, and introducing a context memory mechanism to ensure coherence between chapters; Content optimization and polishing: Perform grammar checking, logic verification, and style optimization on the generated content, and enhance the professionalism of the report by combining it with the domain terminology library; Output and format adjustment: Output the report content in the format required by the user, and automatically generate a table of contents, charts and references.

2. The method according to claim 1, characterized in that The input demand analysis specifically includes: Receive user input on report topic, target audience, word count, field information, and formatting requirements; Natural language understanding technology is used to parse user input requirements, extract key information, and generate structured task descriptions.

3. The method according to claim 1, characterized in that The report framework generation specifically includes: Call the large model to generate chapter division suggestions and content summary for the report; Optimize the framework by combining it with a rule engine or machine learning model; Personalize the framework based on user historical preferences or template data.

4. The method according to claim 3, characterized in that Use the rule engine to check the rationality of chapter division; The frameworks are scored using a machine learning model to select the optimal framework solution.

5. The method according to claim 1, wherein The content generation and filling specifically include: The large model is used to generate detailed content chapter by chapter, and a context memory mechanism is introduced to avoid duplication or contradiction. The domain knowledge base is combined to ensure the professionalism and accuracy of the generated content. The report content is gradually improved through multiple rounds of iterative mechanisms.

6. The method according to claim 5, characterized in that According to the report framework, the large model is called chapter by chapter to generate detailed content, including: Generate one or more paragraphs of text for each chapter, ensuring the content is consistent with the chapter outline; During the generation process, a context memory mechanism is introduced to record the generated content to avoid duplication or contradiction.

7. The method according to claim 1, characterized in that The content optimization and polishing steps include: Use grammar checker to correct spelling, grammar, and punctuation errors; Logical verification is performed through rule engines and machine learning models to ensure that the content is logical and conflict-free; the style of report content is optimized based on the domain terminology library and user preferences.

8. The method according to claim 7, characterized in that Combine the domain terminology library and user preferences to optimize the style of the report content, including: Use domain terminology to replace common vocabulary and improve the professionalism of reports; Adjust the report's language style based on user preferences; Generate more elegant expressions through large models to improve report readability.

9. The method according to claim 1, characterized in that The output and format adjustment specifically include: Convert report content into specified formats according to user needs, including Word, PDF, and Markdown; Automatically generate auxiliary content.

10. The method according to claim 9, characterized in that According to user needs, the report content is converted into a specified format, including: Use document processing tools to convert formats. During the export process, the report's chapter structure, heading styles, and paragraph formatting are preserved; Automatically generate auxiliary content for reports, including: Table of Contents: Automatically generate a table of contents based on chapter titles and support hyperlink jumps; Charts: Automatically generate relevant charts based on the report content; References: Automatically generate a reference list based on the cited content and support multiple citation formats.

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