Research on Intelligent Judgment of Criminal Cases Based on Large Language Models

The intelligentization of criminal case trials has been a hot research topic in the development of digital courts.In the conventional method based on natural language processing,the model directly predicts the final judgment based on the facts of the case.However,when dealing with complex criminal cases,the model may fail to identify the logical dependencies between legal elements and to clearly present the legal reasoning process.The intelligent criminal case trial method based on large language models proposed in this paper follows the approach of “annotating case corpus-pre-training large language model-reinforcing trial logic”.The first step is to annotate the legal elements of the case such as subjects,objects,subjective elements,and objective elements by combining automated annotating with manual correction and create a structured reasoning dataset.The second step is to use ChatGLM3-6b-32k as the foundational large language model for incremental pre-training based on the GLM pre-training framework.The last step is to fine-tune the parameter and increase legal knowledge using the LoRA parameter-efficient fine-tuning strategy and large language model retrieval enhancement technology,thereby reinforcing the trial logic.Experimental results indicate that,compared to Qwen-7B-Chat and Baichuan2-7B-Chat,the ChatGLM3-6b-32k model exhibits superior performance after supervised fine-tuning.The introduction of judicial syllogism significantly enhances the logicality of the judgment texts,ma-king them closer to the reasoning of human judges.In the tasks of charge prediction and sentencing prediction,the model created using this method shows a significant improvement in accuracy compared to the MTL-Fusion,Lawformer,and BERT models.In addition,compared to Legal-BERT and CaseLawBERT,which are trained on European and American legal texts,the ChatGLM3-6b-32k model better suits the trial logic of Chinese criminal cases and demonstrates stronger capabilities in handling long texts.This paper not only explores the application of large language models in intelligent criminal case trials,but also provides valuable references for research on large language models in justice.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-05-01
DOI
https://doi.org/10.11896/jsjkx.241100100
Primary Topic
Sociopolitical Dynamics in Nepal
Type
article
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article

Research on Intelligent Judgment of Criminal Cases Based on Large Language Models

SHILAP Revista de lepidopterología
Sociopolitical Dynamics in Nepal
article

Research on Intelligent Judgment of Criminal Cases Based on Large Language Models

article en

Abstract

The intelligentization of criminal case trials has been a hot research topic in the development of digital courts.In the conventional method based on natural language processing,the model directly predicts the final judgment based on the facts of the case.However,when dealing with complex criminal cases,the model may fail to identify the logical dependencies between legal elements and to clearly present the legal reasoning process.The intelligent criminal case trial method based on large language models proposed in this paper follows the approach of “annotating case corpus-pre-training large language model-reinforcing trial logic”.The first step is to annotate the legal elements of the case such as subjects,objects,subjective elements,and objective elements by combining automated annotating with manual correction and create a structured reasoning dataset.The second step is to use ChatGLM3-6b-32k as the foundational large language model for incremental pre-training based on the GLM pre-training framework.The last step is to fine-tune the parameter and increase legal knowledge using the LoRA parameter-efficient fine-tuning strategy and large language model retrieval enhancement technology,thereby reinforcing the trial logic.Experimental results indicate that,compared to Qwen-7B-Chat and Baichuan2-7B-Chat,the ChatGLM3-6b-32k model exhibits superior performance after supervised fine-tuning.The introduction of judicial syllogism significantly enhances the logicality of the judgment texts,ma-king them closer to the reasoning of human judges.In the tasks of charge prediction and sentencing prediction,the model created using this method shows a significant improvement in accuracy compared to the MTL-Fusion,Lawformer,and BERT models.In addition,compared to Legal-BERT and CaseLawBERT,which are trained on European and American legal texts,the ChatGLM3-6b-32k model better suits the trial logic of Chinese criminal cases and demonstrates stronger capabilities in handling long texts.This paper not only explores the application of large language models in intelligent criminal case trials,but also provides valuable references for research on large language models in justice.

SHILAP Revista de lepidopterología
China University of Political Science and Law (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 29%
Sociopolitical Dynamics in Nepal
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Research on Intelligent Judgment of Criminal Cases Based on Large Language Models · SHILAP Revista de lepidopterología (2025) | TGRS Research Map | TGRS