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        <title>Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction</title>
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        <namePart type="given">Chenlong</namePart>
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    <abstract>Legal judgment prediction is essential for enhancing judicial efficiency. In this work, we identify that existing large language models (LLMs) underperform in this domain due to challenges in understanding case complexities and distinguishing between similar charges. To adapt LLMs for effective legal judgment prediction, we introduce the Ask-Discriminate-Predict (ADAPT) reasoning framework inspired by human judicial reasoning. ADAPT involves decomposing case facts, discriminating among potential charges, and predicting the final judgment. We further enhance LLMs through fine-tuning with multi-task synthetic trajectories to improve legal judgment prediction accuracy and efficiency under our ADAPT framework. Extensive experiments conducted on two widely-used datasets demonstrate the superior performance of our framework in legal judgment prediction, particularly when dealing with complex and confusing charges.</abstract>
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%0 Conference Proceedings
%T Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction
%A Deng, Chenlong
%A Mao, Kelong
%A Zhang, Yuyao
%A Dou, Zhicheng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F deng-etal-2024-enabling
%X Legal judgment prediction is essential for enhancing judicial efficiency. In this work, we identify that existing large language models (LLMs) underperform in this domain due to challenges in understanding case complexities and distinguishing between similar charges. To adapt LLMs for effective legal judgment prediction, we introduce the Ask-Discriminate-Predict (ADAPT) reasoning framework inspired by human judicial reasoning. ADAPT involves decomposing case facts, discriminating among potential charges, and predicting the final judgment. We further enhance LLMs through fine-tuning with multi-task synthetic trajectories to improve legal judgment prediction accuracy and efficiency under our ADAPT framework. Extensive experiments conducted on two widely-used datasets demonstrate the superior performance of our framework in legal judgment prediction, particularly when dealing with complex and confusing charges.
%U https://aclanthology.org/2024.findings-emnlp.43
%P 784-796

Markdown (Informal)

[Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction](https://aclanthology.org/2024.findings-emnlp.43) (Deng et al., Findings 2024)

• Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction (Deng et al., Findings 2024)

ACL

• Chenlong Deng, Kelong Mao, Yuyao Zhang, and Zhicheng Dou. 2024. Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 784–796, Miami, Florida, USA. Association for Computational Linguistics.