Contract review method and device based on large model, medium and equipment

Abstract

The invention provides a contract review method and device based on a large model, a medium and equipment. The method comprises the steps of obtaining a to-be-examined contract document; converting the format of the contract document to be examined into a format which can be identified by an information extraction large model; inputting a to-be-examined contract document after format conversion into the information extraction large model to obtain contract element information in the to-be-examined contract document; providing a contract previewing function and an element positioning function on a page to display the contract document to be examined, and positioning the contract element information on the displayed contract document to be examined; and after a user modifies the contract element information on the page, correspondingly modifying the contract document to be examined. According to the invention, the contract review efficiency is improved, the labor cost is reduced, omissions or errors caused by human factors are reduced, and the accuracy and reliability of contract review are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of contract review, and in particular to a large model-based contract review method and device, medium, and equipment. Background Art

[0002] In the current business environment, contracts are the basic documents for legal and commercial transactions, and the accuracy and completeness of their contents are crucial to protecting the rights and interests of all parties. Traditional contract review methods rely on manual reading and analysis, which is not only time-consuming and labor-intensive, but also prone to omissions or errors due to human factors. With the rapid development of big data and artificial intelligence technologies, the use of automated means to extract key contract information has become a hot topic in the industry. Summary of the invention

[0003] In response to at least one of the above technical problems, an embodiment of the present invention provides a contract review method, device, medium, and equipment based on a large model.

[0004] According to a first aspect, a contract review method based on a large model provided by an embodiment of the present invention includes:

[0005] Obtain contract documents for review;

[0006] Converting the format of the contract document to be reviewed into a format that can be recognized by the information extraction model;

[0007] Inputting the format-converted contract document to be reviewed into the information extraction model to obtain the contract element information in the contract document to be reviewed; wherein the information extraction model is used to extract the contract element information from the contract document to be reviewed;

[0008] Providing a contract preview function and an element positioning function on the page to display the contract document to be reviewed, and positioning the contract element information on the displayed contract document to be reviewed;

[0009] When the user modifies the contract element information on the page, the contract document to be reviewed is modified accordingly.

[0010] In one embodiment, the format recognizable by the information extraction model is docx format. Correspondingly, converting the format of the contract document to be reviewed into a format recognizable by the information extraction model includes at least one of the following:

[0011] If the format of the contract document to be reviewed is doc format, convert the format of the contract document to be reviewed into docx format;

[0012] If the format of the contract document to be reviewed is pdf format, optical character recognition technology is used to convert the format of the contract document to be reviewed into docx format.

[0013] In one embodiment, the information extraction model is also used to analyze whether the extracted contract element information has semantic risks and output the analysis results; wherein the semantic risks include sentence ambiguity and / or vague expression.

[0014] In one embodiment, the information extraction model is also used to: if a table exists in the contract document to be reviewed, analyze whether the total amount and / or total quantity in the table is correct, and output the analysis result.

[0015] In one embodiment, the method further comprises:

[0016] Providing an element extraction feedback function so that users can mark and provide feedback on the contract element information that is incorrectly extracted by the information extraction model;

[0017] After receiving the annotated data fed back by the user, the information extraction model is tuned.

[0018] In one embodiment, the contract element information includes at least one of the information of the two parties to the contract, the contract amount, the service scope and the payment conditions.

[0019] In one embodiment, the training process of the information extraction large model includes:

[0020] Forming a plurality of training samples, and dividing the training samples into a training set, a validation set, and a test set; wherein each training sample is a contract document and contract element information in the contract document;

[0021] Select a pre-trained model;

[0022] Select an adjustment strategy, which includes partial adjustment and full adjustment;

[0023] Set the hyperparameters used in the training process;

[0024] Initializing the parameters of the pre-trained model according to the adjustment strategy;

[0025] The pre-trained model is trained using the training set and the adjustment strategy, the model is regularly evaluated using the validation set during the training process, and adjustments are continued based on the evaluation results to obtain a converged information extraction model;

[0026] The test set is used to perform performance evaluation on the information extraction large model to obtain performance indicators of the information extraction large model.

[0027] According to a second aspect, a contract review device based on a large model provided by an embodiment of the present invention includes:

[0028] Contract acquisition module, used to obtain contract documents to be reviewed;

[0029] A format conversion module, used to convert the format of the contract document to be reviewed into a format that can be recognized by the information extraction model;

[0030] A model extraction module, used to input the format-converted contract document to be reviewed into the information extraction model to obtain the contract element information in the contract document to be reviewed; wherein the information extraction model is used to extract the contract element information from the contract document to be reviewed;

[0031] A human-computer interaction module, used to provide a contract preview function and an element positioning function on the page, so as to display the contract document to be reviewed, and to locate the position of the contract element information on the displayed contract document to be reviewed;

[0032] The contract modification module is used to make corresponding modifications to the contract document to be reviewed after the user modifies the contract element information on the page.

[0033] In one embodiment, the format that the information extraction model can recognize is the docx format. Correspondingly, the format conversion module is used to perform at least one of the following:

[0034] If the format of the contract document to be reviewed is doc format, convert the format of the contract document to be reviewed into docx format;

[0035] If the format of the contract document to be reviewed is pdf format, optical character recognition technology is used to convert the format of the contract document to be reviewed into docx format.

[0036] In one embodiment, the information extraction model is also used to analyze whether the extracted contract element information has semantic risks and output the analysis results; wherein the semantic risks include sentence ambiguity and / or vague expression.

[0037] In one embodiment, the information extraction model is also used to: if a table exists in the contract document to be reviewed, analyze whether the total amount and / or total quantity in the table is correct, and output the analysis result.

[0038] In one embodiment, the device may further include:

[0039] The annotation feedback module is used to provide an element extraction feedback function so that the user can annotate and provide feedback on the contract element information incorrectly extracted by the information extraction model; after receiving the annotation data fed back by the user, the information extraction model is tuned.

[0040] In one embodiment, the contract element information includes at least one of the information of the two parties to the contract, the contract amount, the service scope and the payment conditions.

[0041] In one embodiment, the device may further include:

[0042] The model training module is used to perform the following steps: form multiple training samples, and divide the training samples into a training set, a validation set and a test set; wherein each training sample is a contract document and the contract element information in the contract document; select a pre-trained model; select an adjustment strategy, the adjustment strategy includes partial adjustment and full adjustment; set hyperparameters used in the training process; initialize the parameters of the pre-trained model according to the adjustment strategy; train the pre-trained model using the training set and the adjustment strategy, and perform regular model evaluation using the validation set during the training process, and continue to adjust according to the evaluation results to obtain a converged information extraction model; perform performance evaluation on the information extraction model using the test set to obtain performance indicators of the information extraction model.

[0043] According to a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method provided in the first aspect.

[0044] According to a fourth aspect, an embodiment of the present invention provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method provided in the first aspect is implemented.

[0045] The contract review method, device, medium, and equipment based on the big model provided in the embodiment of the present invention convert the format of the contract document to be reviewed into a format that can be recognized by the information extraction big model, and then input the format-converted contract document to be reviewed into the information extraction big model to obtain the contract element information in the contract document to be reviewed, and then display the contract document to be reviewed through the contract preview function and element positioning function provided on the page, and locate the contract element information on the displayed contract document to be reviewed, so that the user can review the contract element information, and if the user finds that there is an error in the contract element information, the user can modify it on the page. After the modification, the contract to be reviewed has also completed the corresponding modification. It can be seen that the embodiment of the present invention realizes the automatic extraction of contract element information based on the big model. Compared with the traditional manual review method, the embodiment of the present invention can quickly identify and extract key information in the contract through automated means, which greatly shortens the time period of contract review, that is, improves the efficiency of contract review, reduces labor costs, and reduces omissions or errors caused by human factors, and improves the accuracy and reliability of contract review. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of a contract review method based on a large model in one embodiment of the present invention;

[0047] Figure 2 The present invention is a block diagram of a contract review device based on a large model in one embodiment of the present invention. DETAILED DESCRIPTION

[0048] In a first aspect, an embodiment of the present invention provides a contract review method based on a large model, see Figure 1 The method comprises the following steps S110 to S150:

[0049] S110. Obtain the contract documents to be reviewed;

[0050] Among them, the method provided by the embodiment of the present invention can be executed by an inference server.

[0051] S120, converting the format of the contract document to be reviewed into a format that can be recognized by the information extraction model;

[0052] In one embodiment, the format recognizable by the information extraction model is docx format. Accordingly, the step of converting the format of the contract document to be reviewed into a format recognizable by the information extraction model in S120 may include at least one of the following:

[0053] (1) If the format of the contract document to be reviewed is doc format, convert the format of the contract document to be reviewed into docx format;

[0054] (2) If the format of the contract document to be reviewed is pdf format, optical character recognition technology is used to convert the format of the contract document to be reviewed into docx format.

[0055] It can be seen that if the format of the contract document to be reviewed is doc format, it can be converted to docx format using the contract format conversion program. If the format of the contract document to be reviewed is pdf format, it is converted to docx format using optical character recognition technology. This makes it easier for the information extraction model to perform accurate recognition.

[0056] S130, inputting the format-converted contract document to be reviewed into the information extraction macromodel to obtain the contract element information in the contract document to be reviewed; wherein the information extraction macromodel is used to extract the contract element information from the contract document to be reviewed;

[0057] It can be seen that the role of the information extraction model is to extract contract element information from the contract document to be reviewed.

[0058] The contract element information may include at least one of the information of the two parties to the contract, the contract amount, the scope of services and the payment terms.

[0059] In one embodiment, the information extraction model can also be used to analyze whether the extracted contract element information has semantic risks and output the analysis results; wherein the semantic risks include sentence ambiguity and / or vague expression.

[0060] It can be seen that in addition to outputting contract element information, the information extraction model will also analyze whether there is a semantic risk in the contract element information. For example, it will determine whether there is a risk of semantic ambiguity or vague expression in the contract requirement information. After obtaining the analysis results, the analysis results will be output. In this way, users can know whether there is a semantic risk in the contract element information and realize the review of semantic risks.

[0061] In one embodiment, the information extraction model can also be used for: if a table exists in the contract document to be reviewed, analyzing whether the total amount and / or total quantity in the table is correct, and outputting the analysis result.

[0062] It can be seen that in addition to outputting contract elements, the information extraction model can also check whether there are tables in the contract document to be reviewed. If there are tables, the total amount, total quantity and other data in the table will be analyzed to see if they are correct, and the analysis results will be output. In this way, it can be determined whether there are errors in the total value in the table, and the total value can be reviewed.

[0063] S140, providing a contract preview function and an element positioning function on the page to display the contract document to be reviewed, and positioning the contract element information on the displayed contract document to be reviewed;

[0064] S150. After the user modifies the contract element information on the page, the contract document to be reviewed is modified accordingly.

[0065] It can be seen that the contract preview function can display the contract document to be reviewed, so that users can preview the contract document to be reviewed. The element positioning function can locate the extracted contract element information on the contract document to be reviewed. For example, the contract element information on the contract document to be reviewed can be highlighted with a highlight color, so that users can clearly see the contract element information on the contract document to be reviewed, and users can review the contract requirement information. If errors are found during the review, the contract element information can be modified directly on the contract document to be reviewed. After the modification, the contract document to be reviewed will be modified accordingly.

[0066] It can be seen that the system has the function of previewing the contract and modifying the original text.

[0067] In one embodiment, the method may further include:

[0068] An element extraction feedback function is provided to enable users to mark and provide feedback on contract element information that is incorrectly extracted by the information extraction model; after receiving the marked data fed back by the user, the information extraction model is tuned.

[0069] It can be seen that through the element extraction feedback function, users can mark and provide feedback on the contract element information that is incorrectly extracted by the information extraction model. For example, the extracted contract element information is not the real key information, that is, the information extraction is unreasonable. After receiving feedback, the information extraction model is tuned to improve the extraction accuracy of the information extraction model.

[0070] In actual scenarios, the system also has a contract management function, through which users can upload, delete, download and other operations on contracts.

[0071] In one embodiment, the training process of the information extraction model includes the following 7 steps:

[0072] 1. Form multiple training samples, and divide the training samples into a training set, a validation set, and a test set; wherein each training sample is a contract document and contract element information in the contract document;

[0073] 2. Select a pre-trained model;

[0074] 3. Select an adjustment strategy, which includes partial adjustment and full adjustment;

[0075] 4. Set the hyperparameters used in the training process;

[0076] 5. Initialize the parameters of the pre-trained model according to the adjustment strategy;

[0077] 6. Using the training set and the adjustment strategy to train the pre-trained model, using the validation set to regularly evaluate the model during the training process, and continuing to adjust according to the evaluation results to obtain a converged information extraction model;

[0078] 7. Use the test set to perform performance evaluation on the information extraction model to obtain performance indicators of the information extraction model.

[0079] It can be seen that the training process of the information extraction model can roughly include:

[0080] (1) First, prepare a contract information set including multiple contract documents, and then determine the contract element information of each contract based on the original contract text, such as Party A and Party B (lessee, authorized party, contractor, principal, etc. are all Party A and Party B information), amount, tax, account, etc. The contract element information of each contract is marked on the contract to form a training sample, so that multiple training samples will be obtained, and the multiple training samples are divided into a training set, a validation set, and a test set.

[0081] (2) Then, according to the characteristics of the contract and the extraction objectives, a suitable pre-trained model is selected.

[0082] (3) Next, select an appropriate fine-tuning strategy based on task requirements and available resources. Consider whether to make full or partial adjustments, and even the level and scope of the adjustments.

[0083] (4) Set the hyperparameters used in the training process, such as learning rate, batch size, number of training rounds, etc. The choice of these hyperparameters has an important impact on the performance and convergence speed of fine-tuning.

[0084] (5) Initialize the parameters of the pre-trained model. For full adjustment, all model parameters will be randomly initialized; for partial adjustment, only the parameters of the top layer or a few layers will be randomly initialized.

[0085] (6) Use the prepared training set and adjustment strategy to train the pre-trained model. During the training process, the model is regularly evaluated using the validation set and the hyperparameters are adjusted based on the evaluation results.

[0086] (7) After training is completed, the final model is evaluated using the test set to obtain the final performance indicators.

[0087] After the training is completed, the obtained information extraction model will be deployed on the inference server for extracting contract element information in the contract document.

[0088] In recent years, deep learning models, especially large-scale pre-trained models (such as BERT, GPT series, etc.) have made remarkable achievements in the field of natural language processing. These models can understand and generate complex text content, providing new possibilities for the automated extraction of contract information. By training these models, they can learn to identify key elements in the contract text, such as information about both parties to the contract, signing date, scope of services, payment terms, liability for breach of contract, etc., thereby greatly improving the efficiency and accuracy of contract review. However, contract texts are often highly professional, complex and diverse, containing a large number of legal terms and expressions for specific industries, which puts higher requirements on the generalization and understanding capabilities of the model. In addition, the key information in the contract is often hidden in long paragraphs of text, and the structure and format of different contracts vary greatly. How to effectively and accurately extract key information from these unstructured data has become the main challenge faced by the current contract key information extraction method based on large models. The embodiment of the present invention obtains a large information extraction model through the above training process, which can efficiently and accurately extract key information from various contract texts, which is of great significance for improving the efficiency of contract review and reducing legal risks.

[0089] The embodiment of the present invention proposes a method for extracting key contract information based on a large model. The method takes the language large model as the core and integrates advanced artificial intelligence technology. It aims to provide users with a more intelligent, convenient and personalized information acquisition and interactive experience, so that information extraction does not have to rely on manual extraction, which greatly improves the review efficiency. Due to the integration of technologies such as OCR text recognition and language large models, the large model is used to deeply understand and analyze the contract, thereby realizing the extraction, collation and review analysis of key contract information. The embodiment of the present invention supports batch processing of contracts, and it takes an average of 20 seconds per contract, which is a huge improvement in information processing speed compared to the previous manual 20 contracts per person per week. It can be seen that the method provided by the embodiment of the present invention is convenient, fast, labor-saving and efficient, and can parse contract documents in batches to quickly extract contract element information.

[0090] The embodiments of the present invention have the following beneficial effects:

[0091] (1) Compared with the traditional manual review method, the method for extracting key contract information based on a large model proposed in the embodiment of the present invention can quickly identify and extract key information in the contract through automated means, thus greatly shortening the contract review period. This not only reduces labor costs, but also enables enterprises to respond to market changes more quickly and seize business opportunities.

[0092] (2) With the help of deep learning models, especially the powerful capabilities of large-scale pre-training models, the embodiments of the present invention can accurately understand the complexity and diversity of contract texts and effectively identify and extract key information hidden in long paragraphs. This reduces omissions or errors caused by human factors and improves the accuracy and reliability of contract review.

[0093] (3) By accurately extracting key information from the contract, the embodiments of the present invention can help enterprises promptly discover potential legal risk points, such as unclear payment terms, ambiguous liability for breach of contract, etc. This helps enterprises take measures in advance to prevent legal risks and protect their own rights and interests.

[0094] (4) The method provided by the embodiment of the present invention has high flexibility and scalability, and can be customized according to the specific needs of different enterprises. For example, for the contract text of a specific industry, a special model can be trained to extract key information related to the industry, thereby better meeting the actual needs of the enterprise.

[0095] (5) The embodiments of the present invention represent an important step forward in the field of contract review towards intelligence and automation. With the continuous advancement of technology and the expansion of application scenarios, the method provided by the embodiments of the present invention is expected to become one of the mainstream means in the field of contract review in the future, providing strong support for corporate compliance operations and risk management.

[0096] In summary, the large model-based contract key information extraction method of the present invention realizes batch analysis of key information and semantic risks of contracts and displays them, thereby greatly liberating human resources in professional fields, reducing professional requirements for human resources, greatly shortening the time for contract key information extraction and semantic risk analysis, and being able to provide timely and accurate guidance for enterprises to determine the direction of research and development. Moreover, it has significant advantages in improving review efficiency, accuracy, legal risk prevention and control capabilities, and supports customized needs, promoting the intelligent development of the contract review field.

[0097] In a second aspect, an embodiment of the present invention provides a contract review device based on a large model, see Figure 2 , the device 100 comprises:

[0098] The contract acquisition module 110 is used to acquire the contract document to be reviewed;

[0099] A format conversion module 120, used to convert the format of the contract document to be reviewed into a format that can be recognized by the information extraction model;

[0100] The model extraction module 130 is used to input the format-converted contract document to be reviewed into the information extraction model to obtain the contract element information in the contract document to be reviewed; wherein the information extraction model is used to extract the contract element information from the contract document to be reviewed;

[0101] The human-computer interaction module 140 is used to provide a contract preview function and an element positioning function on the page to display the contract document to be reviewed, and to locate the contract element information on the displayed contract document to be reviewed;

[0102] The contract modification module 150 is used to modify the contract document to be reviewed accordingly after the user modifies the contract element information on the page.

[0103] In one embodiment, the format that the information extraction model can recognize is the docx format. Correspondingly, the format conversion module 120 is used to perform at least one of the following:

[0104] If the format of the contract document to be reviewed is doc format, convert the format of the contract document to be reviewed into docx format;

[0105] If the format of the contract document to be reviewed is pdf format, optical character recognition technology is used to convert the format of the contract document to be reviewed into docx format.

[0106] In one embodiment, the information extraction model is also used to analyze whether the extracted contract element information has semantic risks and output the analysis results; wherein the semantic risks include sentence ambiguity and / or vague expression.

[0107] In one embodiment, the information extraction model is also used to: if a table exists in the contract document to be reviewed, analyze whether the total amount and / or total quantity in the table is correct, and output the analysis result.

[0108] In one embodiment, the device may further include:

[0109] The annotation feedback module is used to provide an element extraction feedback function so that the user can annotate and provide feedback on the contract element information incorrectly extracted by the information extraction model; after receiving the annotation data fed back by the user, the information extraction model is tuned.

[0110] In one embodiment, the contract element information includes at least one of the information of the two parties to the contract, the contract amount, the service scope and the payment conditions.

[0111] In one embodiment, the device may further include:

[0112] The model training module is used to perform the following steps: form multiple training samples, and divide the training samples into a training set, a validation set and a test set; wherein each training sample is a contract document and the contract element information in the contract document; select a pre-trained model; select an adjustment strategy, the adjustment strategy includes partial adjustment and full adjustment; set hyperparameters used in the training process; initialize the parameters of the pre-trained model according to the adjustment strategy; train the pre-trained model using the training set and the adjustment strategy, and perform regular model evaluation using the validation set during the training process, and continue to adjust according to the evaluation results to obtain a converged information extraction model; perform performance evaluation on the information extraction model using the test set to obtain performance indicators of the information extraction model.

[0113] It is understandable that the explanation, specific implementation, beneficial effects, examples, etc. of the relevant contents in the device provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0114] In a third aspect, an embodiment of the present invention provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the method provided in the first aspect.

[0115] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0120] It is understandable that the explanation, specific implementation methods, beneficial effects, examples, etc. of the relevant contents in the computer-readable medium provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0121] In a fourth aspect, an embodiment of the present specification provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method in any embodiment of the specification.

[0122] It is understandable that the explanation, specific implementation, beneficial effects, examples, etc. of the relevant contents in the computing device provided in the embodiment of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0123] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0124] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, widgets, or any combination thereof. When implemented by software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0125] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A contract review method based on a large model, characterized in that: include: Obtain contract documents for review; Converting the format of the contract document to be reviewed into a format that can be recognized by the information extraction model; Inputting the format-converted contract document to be reviewed into the information extraction model to obtain the contract element information in the contract document to be reviewed; wherein the information extraction model is used to extract the contract element information from the contract document to be reviewed; Providing a contract preview function and an element positioning function on the page to display the contract document to be reviewed, and positioning the contract element information on the displayed contract document to be reviewed; When the user modifies the contract element information on the page, the contract document to be reviewed is modified accordingly.

2. The method according to claim 1, characterized in that The format that the information extraction model can recognize is the docx format. Correspondingly, converting the format of the contract document to be reviewed into a format that the information extraction model can recognize includes at least one of the following: If the format of the contract document to be reviewed is doc format, convert the format of the contract document to be reviewed into docx format; If the format of the contract document to be reviewed is pdf format, optical character recognition technology is used to convert the format of the contract document to be reviewed into docx format.

3. The method according to claim 1, characterized in that The information extraction model is also used to analyze whether the extracted contract element information has semantic risks and output the analysis results; wherein the semantic risks include sentence ambiguity and / or vague expression.

4. The method according to claim 1, characterized in that The information extraction model is also used to: if there is a table in the contract document to be reviewed, analyze whether the total amount and / or total quantity in the table is correct, and output the analysis result.

5. The method according to claim 1, characterized in that Also includes: Providing an element extraction feedback function so that users can mark and provide feedback on the contract element information that is incorrectly extracted by the information extraction model; After receiving the annotated data fed back by the user, the information extraction model is tuned.

6. The method according to claim 1, characterized in that The contract element information includes at least one of the information of the two parties to the contract, the contract amount, the service scope and the payment conditions.

7. The method according to claim 1, characterized in that The training process of the information extraction large model includes: Forming a plurality of training samples, and dividing the training samples into a training set, a validation set, and a test set; wherein each training sample is a contract document and contract element information in the contract document; Select a pre-trained model; Select an adjustment strategy, which includes partial adjustment and full adjustment; Set the hyperparameters used in the training process; Initializing the parameters of the pre-trained model according to the adjustment strategy; The pre-trained model is trained using the training set and the adjustment strategy, the model is regularly evaluated using the validation set during the training process, and adjustments are continued based on the evaluation results to obtain a converged information extraction model; The test set is used to perform performance evaluation on the information extraction large model to obtain performance indicators of the information extraction large model.

8. A contract review device based on a large model, characterized in that: include: Contract acquisition module, used to obtain contract documents to be reviewed; A format conversion module, used to convert the format of the contract document to be reviewed into a format that can be recognized by the information extraction model; A model extraction module, used to input the format-converted contract document to be reviewed into the information extraction model to obtain the contract element information in the contract document to be reviewed; wherein the information extraction model is used to extract the contract element information from the contract document to be reviewed; A human-computer interaction module, used to provide a contract preview function and an element positioning function on the page, so as to display the contract document to be reviewed, and to locate the position of the contract element information on the displayed contract document to be reviewed; The contract modification module is used to make corresponding modifications to the contract document to be reviewed after the user modifies the contract element information on the page.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

10. A computing device, characterized in that The method comprises a memory and a processor, wherein the memory stores executable codes, and when the processor executes the executable codes, the method according to any one of claims 1 to 7 is implemented.

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