Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction[Paper]
Released Resources
- 🤗The Huggingface model: Based on Qwen2-7B, we trained a model using the CAIL2018 dataset. Qwen2-7B-CAIL2018-step-8765
- The training trajectories: We release the 80,141 training trajectories of the CAIL2018 dataset in this link
Supported Prompts
❗️Note: Our released model needs the Qwen chat_template
to conduct correct generation.
We support the following four prompts to enable reasoning. You should use the same input format and prompt
to achieve the best performance.
Prompt 1: ADAPT Reasoning
case_input = f"案件描述:{description}\n被告人姓名:{defendant_name}"
prompt = "请你采用ADAPT框架分析以上案件中该被告人可能被判处的罪名、适用法条和刑期"
model_input_str = '\n'.join(case_input, prompt)
Prompt 2: Ask
case_input = f"案件描述:{description}\n被告人姓名:{defendant_name}"
prompt = "请你用法律理论分析以上案件中该被告人在行为主体,起因、行为和结果,行为对象,犯罪主观四个方面的信息"
model_input_str = '\n'.join(case_input, prompt)
Prompt 3: Article
case_input = f"案件描述:{description}\n被告人姓名:{defendant_name}"
prompt = "请你依次列出以上案件中被告人适用的法条具体内容,以及适用该法条的原因"
model_input_str = '\n'.join(case_input, prompt)
Prompt 4: Sentencing factors
case_input = f"案件描述:{description}\n被告人姓名:{defendant_name}\n罪名:{crimes}" # e.g., 污染环境罪
prompt = "请你分析以上案件中的量刑区间和量刑因素,并给出最后的量刑预测结果"
model_input_str = '\n'.join(case_input, prompt)
Citation
@misc{deng2024enablingdiscriminativereasoningllms,
title={Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction},
author={Chenlong Deng and Kelong Mao and Yuyao Zhang and Zhicheng Dou},
year={2024},
eprint={2407.01964},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.01964},
}
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