Contract risk assessment and compliance check method, system and equipment based on large language model, and medium

Abstract

The invention discloses a contract risk assessment and compliance check method, system and device based on a large language model and a medium, belongs to the cross technical field of artificial intelligence and natural language processing, and aims to solve the technical problem of how to automatically, quickly and accurately identify potential risks and compliance in a contract. According to the technical scheme, the method comprises the steps of data preprocessing, wherein non-plain text format files of various sources are subjected to data preprocessing and converted into plain text format files; constructing a knowledge performing semantic understanding and relation extraction on industrial standards, internal regulations of enterprises and a plain text format file obtained after preprocessing by utilizing a big language, and constructing a structured and queriable knowledge base; training a large language model: optimizing parameters of the large language model in combination with pre-training and supervised training; and risk assessment and compliance check: based on the trained large language model and the compliance rule base, performing identification and analysis of potential risk points and compliance check on the input contract text.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of artificial intelligence and natural language processing, and specifically to a method, system, device and medium for contract risk assessment and compliance checking based on a large language model. Background Art

[0002] In the current complex and ever-changing business environment, contract risk assessment and compliance checks play a vital role. They are key links in ensuring that companies can operate legally and in compliance with regulations, effectively avoid legal risks, and safeguard their own rights and interests.

[0003] Traditional contract risk assessment and compliance checks mainly rely on manual review, which is not only time-consuming, labor-intensive and inefficient, but also prone to inconsistencies in review results due to differences in reviewers’ personal experience, professional knowledge and subjective judgment, making it difficult to ensure the accuracy and objectivity of the review.

[0004] Therefore, how to automatically, quickly and accurately identify potential risks and compliance issues in contracts is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical task of the present invention is to provide a contract risk assessment and compliance checking method, system, device and medium based on a large language model to solve the problem of how to automatically, quickly and accurately identify potential risks and compliance issues in contracts.

[0006] The technical task of the present invention is achieved in the following way: a contract risk assessment and compliance inspection method based on a large language model, the method is as follows:

[0007] Data preprocessing: preprocess non-plain text files from various sources and convert them into plain text files;

[0008] Build a knowledge base: Use big language to understand the semantics and extract relationships of industry standards, internal corporate regulations, and plain text files obtained after preprocessing, and build a structured, queryable knowledge base to support risk assessment and compliance checks;

[0009] Training large language models: Combine pre-training and supervised training to optimize large language model parameters and improve the accuracy of risk assessment and compliance checks;

[0010] Risk assessment and compliance check: Based on the trained large language model and compliance rule base, the input contract text is identified and analyzed for potential risk points and compliance checks.

[0011] As a preference, data preprocessing is specifically as follows:

[0012] File acquisition and decoding: For non-plain text contract documents such as PDF files or scanned image files, use OCR counting or PDF parsing technology to accurately convert the content of non-plain text contract documents into plain text format that can be further processed and searched, laying a solid foundation for subsequent analysis work;

[0013] Word segmentation and text cleaning: Use word segmentation tools to split continuous text sequences into meaningful words or phrases, delete words that appear more than a set number of times but contribute little to the meaning of the text, and then normalize the numbers in the text;

[0014] File storage and output: The pre-processed text data is output in markdown format and stored in a secure and reliable storage medium for easy reading and editing; the storage medium includes databases and file systems.

[0015] Preferably, training a large language model is as follows:

[0016] Model training: By integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, model parameters are iteratively adjusted to improve prediction accuracy and generalization capabilities, ensuring the accuracy and efficiency of contract review;

[0017] Model deployment and evaluation: The trained large language model is applied to contract review. The model performance is ensured through large language model conversion, optimization and operation on different platforms. At the same time, the accuracy and precision indicators are used to comprehensively evaluate the model performance, providing an important basis for the continuous optimization and deployment of the model.

[0018] A contract risk assessment and compliance checking system based on a large language model, the system comprising:

[0019] A data preprocessing module is used to preprocess non-plain text format files from various sources and convert them into plain text format files;

[0020] The knowledge base construction module is used to use big language to perform semantic understanding and relationship extraction on industry standards, internal enterprise regulations, and plain text files obtained after preprocessing, and build a structured and queryable knowledge base to support risk assessment and compliance inspection;

[0021] Large language model training module, which is used to optimize large language model parameters by combining pre-training and supervised training to improve the accuracy of risk assessment and compliance inspection;

[0022] The risk assessment and compliance check module is used to identify and analyze potential risk points and perform compliance checks on the input contract text based on the trained large language model and compliance rule base.

[0023] Preferably, the data preprocessing module includes:

[0024] The file acquisition and decoding submodule is used to accurately convert the content of non-plain text contract documents in PDF files or scanned image files into plain text format that can be further processed and searched using OCR counting or PDF parsing technology, laying a solid foundation for subsequent analysis work;

[0025] The word segmentation and text cleaning submodule is used to perform word segmentation using the word segmentation tool, splitting the continuous text sequence into meaningful words or phrases, and deleting words that appear more than a set number of times but contribute little to the meaning of the text, and then normalizing the numbers in the text;

[0026] The file storage and output submodule is used to output the pre-processed text data into markdown format and store it in a safe and reliable storage medium for easy reading and editing; the storage medium includes a database and a file system.

[0027] Preferably, the large language training module includes:

[0028] The model training submodule is used to iteratively adjust model parameters by integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, to improve prediction accuracy and generalization capabilities, and ensure the accuracy and efficiency of contract review;

[0029] The model deployment and evaluation submodule is used to apply the trained large language model to contract review. It ensures model performance by converting, optimizing and running the large language model on different platforms. At the same time, it uses accuracy and precision indicators to comprehensively evaluate model performance, providing an important basis for the continuous optimization and deployment of the model.

[0030] An electronic device comprising: a memory and at least one processor;

[0031] Wherein, the memory stores computer-executable instructions;

[0032] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the above-mentioned contract risk assessment and compliance checking method based on a large language model.

[0033] A computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the above-mentioned contract risk assessment and compliance checking method based on a large language model is implemented.

[0034] The contract risk assessment and compliance checking method, system, device and medium based on a large language model of the present invention have the following advantages:

[0035] (i) The present invention integrates multi-source information such as industry standards, internal enterprise regulations, and pre-processed text data to construct a private knowledge base; then, in the large language model training module, the model is trained using a large-scale corpus to improve its ability to understand contract texts; finally, in risk assessment and compliance checks, the trained large language model is used to perform risk assessment and compliance checks on contract texts, and the assessment results and compliance recommendations are output, which can effectively improve the accuracy and efficiency of contract risk assessment and compliance checks, and provide strong support for the company's contract management work; through the implementation of the present invention, companies can better identify and avoid contract risks, ensure contract compliance, and thereby improve the company's operational efficiency and market competitiveness;

[0036] (II) The present invention uses deep learning technology to build a large language model to achieve efficient understanding and analysis of contract texts; by mining key information in the contract and combining professional knowledge in the fields of law and compliance, the method can automatically and accurately identify potential risks and compliance issues in the contract, and provide intelligent support for the company's contract review. It will significantly improve the efficiency and accuracy of contract review, reduce the company's legal risks and operating costs, and can automatically, quickly and accurately identify potential risks and compliance issues in the contract, providing strong support for the company's compliance operations;

[0037] (III) The present invention can deeply understand the semantics of text and accurately extract key information, thus providing a new, more efficient and reliable intelligent solution for contract risk assessment and compliance inspection;

[0038] (IV) The present invention realizes the automated analysis and processing of contract texts by introducing a large language model, significantly improving the efficiency of contract review. Traditional contract review methods mainly rely on manual work, which is not only time-consuming and labor-intensive, but also easily affected by human factors. The present invention can quickly identify potential risks and compliance issues in contracts, greatly shortening the review cycle and reducing labor costs.

[0039] (V) The present invention utilizes the deep learning and natural language processing capabilities of a large language model to more accurately assess the risks in a contract; by understanding the semantics and extracting features of the contract text, the patent can identify key information related to risk assessment and provide risk levels and assessment recommendations based on this information, which is more accurate and objective than traditional manual assessment methods;

[0040] (VI) The present invention also has a powerful compliance checking function, which can compare and analyze the contract text one by one according to the requirements of relevant laws and regulations, industry standards and internal regulations of the enterprise, and determine whether the contract meets these requirements. This not only helps enterprises comply with laws and regulations and reduce legal risks, but also improves the compliance management level of enterprises and enhances their market competitiveness;

[0041] (VII) The present invention also helps to promote the digital transformation of contract management. By introducing advanced technologies such as large language models, it can realize the automatic collection, analysis and processing of contract data, improve the level of informatization of contract management, and not only help enterprises better understand the contract situation, but also provide strong support for enterprise decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention is further described below in conjunction with the accompanying drawings.

[0043] Attached Figure 1 The flowchart of the contract risk assessment and compliance checking method based on the large language model is shown in FIG. DETAILED DESCRIPTION

[0044] The contract risk assessment and compliance checking method, system, device and medium based on a large language model of the present invention are described in detail below with reference to the drawings and specific embodiments of the specification.

[0045] Embodiment 1:

[0046] As attached Figure 1 As shown, this embodiment provides a contract risk assessment and compliance inspection method based on a large language model, and the method is specifically as follows:

[0047] S1. Data preprocessing: preprocess non-plain text files from various sources and convert them into plain text files;

[0048] S2. Build a knowledge base: Use big language to understand the semantics and extract relationships of industry standards, internal enterprise regulations, and plain text files obtained after preprocessing, and build a structured and queryable knowledge base to support risk assessment and compliance checks;

[0049] S3. Training large language models: Combine pre-training and supervised training to optimize large language model parameters and improve the accuracy of risk assessment and compliance checks;

[0050] S4. Risk assessment and compliance check: Based on the trained large language model and compliance rule base, the input contract text is identified and analyzed for potential risk points and compliance checks are performed.

[0051] The data preprocessing in step S1 of this embodiment is specifically as follows:

[0052] S101. File acquisition and decoding: For non-plain text contract documents such as PDF files or scanned image files, use OCR counting or PDF parsing technology to accurately convert the content of non-plain text contract documents into plain text format that can be further processed and searched, laying a solid foundation for subsequent analysis work;

[0053] S102, word segmentation and text cleaning: Use word segmentation tools to perform word segmentation, split the continuous text sequence into meaningful words or phrases, delete words that appear more than a set number of times in the text but contribute little to the meaning of the text, and then normalize the numbers in the text;

[0054] S103, file storage and output: the pre-processed text data is output to the markdown format and stored in a safe and reliable storage medium for easy reading and editing; wherein the storage medium includes a database and a file system.

[0055] The training of the large language model in step S3 of this embodiment is specifically as follows:

[0056] S301, Model training: By integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, iteratively adjust model parameters to improve prediction accuracy and generalization capabilities, and ensure the accuracy and efficiency of contract review;

[0057] S302, Model deployment and evaluation: Apply the trained large language model to contract review, and ensure model performance through large language model conversion, optimization and operation on different platforms; at the same time, use accuracy and precision indicators to comprehensively evaluate model performance, providing an important basis for the continuous optimization and deployment of the model.

[0058] Embodiment 2:

[0059] This embodiment provides a contract risk assessment and compliance checking system based on a large language model, the system comprising:

[0060] A data preprocessing module is used to preprocess non-plain text format files from various sources and convert them into plain text format files;

[0061] The knowledge base construction module is used to use big language to perform semantic understanding and relationship extraction on industry standards, internal enterprise regulations, and plain text files obtained after preprocessing, and build a structured and queryable knowledge base to support risk assessment and compliance inspection;

[0062] Large language model training module, which is used to optimize large language model parameters by combining pre-training and supervised training to improve the accuracy of risk assessment and compliance inspection;

[0063] The risk assessment and compliance check module is used to identify and analyze potential risk points and perform compliance checks on the input contract text based on the trained large language model and compliance rule base.

[0064] The data preprocessing module in this embodiment includes:

[0065] The file acquisition and decoding submodule is used to accurately convert the content of non-plain text contract documents in PDF files or scanned image files into plain text format that can be further processed and searched using OCR counting or PDF parsing technology, laying a solid foundation for subsequent analysis work;

[0066] The word segmentation and text cleaning submodule is used to perform word segmentation using the word segmentation tool, splitting the continuous text sequence into meaningful words or phrases, and deleting words that appear more than a set number of times but contribute little to the meaning of the text, and then normalizing the numbers in the text;

[0067] The file storage and output submodule is used to output the pre-processed text data into markdown format and store it in a safe and reliable storage medium for easy reading and editing; the storage medium includes a database and a file system.

[0068] The large language training module in this embodiment includes:

[0069] The model training submodule is used to iteratively adjust model parameters by integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, to improve prediction accuracy and generalization capabilities, and ensure the accuracy and efficiency of contract review;

[0070] The model deployment and evaluation submodule is used to apply the trained large language model to contract review. It ensures model performance by converting, optimizing and running the large language model on different platforms. At the same time, it uses accuracy and precision indicators to comprehensively evaluate model performance, providing an important basis for the continuous optimization and deployment of the model.

[0071] Embodiment 3:

[0072] This embodiment also provides an electronic device, including: a memory and at least one processor;

[0073] Wherein, the memory stores computer-executable instructions;

[0074] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the contract risk assessment and compliance checking method based on a large language model as described in any one of the embodiments of the present invention.

[0075] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc.

[0076] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, at least one disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0077] Embodiment 4:

[0078] This embodiment also provides a computer-readable storage medium, which stores a plurality of instructions, which are loaded by a processor, so that the processor executes the contract risk assessment and compliance inspection method based on a large language model in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0079] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0080] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0081] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0082] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A contract risk assessment and compliance inspection method based on a large language model, characterized in that: The method is as follows: Data preprocessing: preprocess non-plain text files from various sources and convert them into plain text files; Build a knowledge base: Use big language to understand the semantics and extract relationships of industry standards, internal corporate regulations, and plain text files obtained after preprocessing, and build a structured, queryable knowledge base to support risk assessment and compliance checks; Training large language models: Combine pre-training and supervised training to optimize large language model parameters and improve the accuracy of risk assessment and compliance checks; Risk assessment and compliance check: Based on the trained large language model and compliance rule base, the input contract text is identified and analyzed for potential risk points and compliance checks.

2. The contract risk assessment and compliance checking method based on a large language model according to claim 1 is characterized in that: The data preprocessing is as follows: File acquisition and decoding: For non-plain text contract documents in PDF files or scanned image files, use OCR counting or PDF parsing technology to accurately convert the content of non-plain text contract documents into plain text format that can be further processed and searched; Word segmentation and text cleaning: Use word segmentation tools to split continuous text sequences into meaningful words or phrases, delete words that appear more than a set number of times but contribute little to the meaning of the text, and then normalize the numbers in the text; File storage and output: The pre-processed text data is output in markdown format and stored in a storage medium; the storage medium includes a database and a file system.

3. The method for contract risk assessment and compliance checking based on a large language model according to claim 1 or 2, characterized in that: Training a large language model is as follows: Model training: By integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, model parameters are iteratively adjusted to improve prediction accuracy and generalization capabilities; Model deployment and evaluation: The trained large language model is applied to contract review. The model performance is ensured through large language model conversion, optimization and operation on different platforms. At the same time, the accuracy and precision indicators are used to comprehensively evaluate the model performance, providing an important basis for the continuous optimization and deployment of the model.

4. A contract risk assessment and compliance checking system based on a large language model, characterized in that: The system includes: A data preprocessing module is used to preprocess non-plain text format files from various sources and convert them into plain text format files; The knowledge base construction module is used to use big language to perform semantic understanding and relationship extraction on industry standards, internal enterprise regulations, and plain text files obtained after preprocessing, and build a structured and queryable knowledge base to support risk assessment and compliance inspection; Large language model training module, which is used to optimize large language model parameters by combining pre-training and supervised training to improve the accuracy of risk assessment and compliance inspection; The risk assessment and compliance check module is used to identify and analyze potential risk points and perform compliance checks on the input contract text based on the trained large language model and compliance rule base.

5. The contract risk assessment and compliance checking system based on a large language model according to claim 4 is characterized in that: The data preprocessing module includes: The file acquisition and decoding submodule is used to accurately convert the content of non-plain text contract documents in PDF files or scanned image files into plain text format that can be further processed and searched using OCR counting or PDF parsing technology; The word segmentation and text cleaning submodule is used to perform word segmentation using the word segmentation tool, splitting the continuous text sequence into meaningful words or phrases, and deleting words that appear more than a set number of times but contribute little to the meaning of the text, and then normalizing the numbers in the text; The file storage and output submodule is used to output the pre-processed text data into markdown format and store it in a storage medium; wherein the storage medium includes a database and a file system.

6. The contract risk assessment and compliance checking system based on a large language model according to claim 4 or 5, characterized in that: The large language training module includes: The model training submodule is used to iteratively adjust model parameters by integrating pre-training and fine-tuned supervised training, combined with high-quality contract data sets, to improve prediction accuracy and generalization capabilities; The model deployment and evaluation submodule is used to apply the trained large language model to contract review. The model performance is ensured by converting, optimizing and running the large language model on different platforms. At the same time, the accuracy and precision indicators are used to comprehensively evaluate the model performance, providing an important basis for the continuous optimization and deployment of the model.

7. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the contract risk assessment and compliance checking method based on a large language model as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the processor executes the computer-executable instructions, the contract risk assessment and compliance checking method based on a large language model as described in any one of claims 1 to A is implemented.

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