LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning

• URL: http://arxiv.org/abs/2506.07443v1
• Date: Mon, 09 Jun 2025 05:48:35 GMT
• Title: LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning
• Authors: Weijie Shi, Han Zhu, Jiaming Ji, Mengze Li, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Sirui Han, Yike Guo,
• Abstract summary: Legal judgment prediction (LJP) aims to function as a judge by making final rulings based on case claims and facts.<n>We propose LegalReasoner, which enhances LJP reliability through step-wise verification and correction of the reasoning process.<n>We release the LegalHK dataset, containing 58,130 Hong Kong court cases with detailed annotations of dispute points, step-by-step reasoning chains, and process verification labels.
• Score: 25.808321575139537
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Legal judgment prediction (LJP) aims to function as a judge by making final rulings based on case claims and facts, which plays a vital role in the judicial domain for supporting court decision-making and improving judicial efficiency. However, existing methods often struggle with logical errors when conducting complex legal reasoning. We propose LegalReasoner, which enhances LJP reliability through step-wise verification and correction of the reasoning process. Specifically, it first identifies dispute points to decompose complex cases, and then conducts step-wise reasoning while employing a process verifier to validate each step's logic from correctness, progressiveness, and potential perspectives. When errors are detected, expert-designed attribution and resolution strategies are applied for correction. To fine-tune LegalReasoner, we release the LegalHK dataset, containing 58,130 Hong Kong court cases with detailed annotations of dispute points, step-by-step reasoning chains, and process verification labels. Experiments demonstrate that LegalReasoner significantly improves concordance with court decisions from 72.37 to 80.27 on LLAMA-3.1-70B. The data is available at https://huggingface.co/datasets/weijiezz/LegalHK.

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