轻量大模型可有效生成法庭观点并辅助定罪预测。
Exploring Lightweight Large Language Models for Court View Generation
- 用小于20亿参数的轻量大模型生成法庭观点
- 轻量模型在定罪预测上表现接近甚至超过传统神经网络
- 适合司法AI研究者和法律科技开发者参考
刑事法庭观点生成（CVG）是法律人工智能中的关键任务，旨在根据案件事实生成法庭观点。本文系统探索了轻量级（参数少于20亿）大语言模型在该任务中的能力及其对定罪预测的影响。研究回答四个核心问题：不同模型架构如何影响生成质量与定罪预测；模型规模的作用；轻量级模型与深度神经网络的对比；先生成法庭观点再预测定罪，是否优于直接预测。同时，我们构建了CVGEvalKit评估框架，包含三个公开数据集及对应的定罪预测任务。在混合训练集上训练，各数据集测试集上评估。实验揭示了模型架构、规模与任务间权衡的新见解，凸显轻量级大模型在司法AI中的潜力。源码已匿名开源。
原文摘要 · Abstract (English)
Criminal Court View Generation (CVG) is a critical task in Legal Artificial Intelligence (Legal AI), involving the generation of court view based on case facts. In this work, we systematically explore the capabilities of lightweight (smaller than 2B) large language models (LLMs) in CVG and their impact on charge prediction. Our study addresses four key questions: (1) how does different architecture of LLMs affect the CVG quality and charge prediction. (2) how does LLMs size contribute to the performance, (3) how do lightweight LLMs compare with Deep Neural Networks (DNNs) in these tasks, and (4) how does predicting charge by court view generation first compare with predicting it directly. Additionally, we also develop CVGEvalKit, an evaluation framework including three public available datasets for CVG tasks, as well as predicting their charges. Comprehensive experiments are conducted on this framework, where models are trained on a mixed training set and evaluated on each dataset's test set. Experimental results provide new insights into the trade-offs between model architecture, model size, and the influence between different tasks, highlighting the potential of lightweight LLMs in judicial AI applications. The source code is anonymously available at \url{https://github.com/ZhitianHou/CVGEvalKit}
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