Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.07443 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913885097820160 |
|---|---|
| author | Shi, Weijie Zhu, Han Ji, Jiaming Li, Mengze Zhang, Jipeng Zhang, Ruiyuan Zhu, Jia Xu, Jiajie Han, Sirui Guo, Yike |
| author_facet | Shi, Weijie Zhu, Han Ji, Jiaming Li, Mengze Zhang, Jipeng Zhang, Ruiyuan Zhu, Jia Xu, Jiajie Han, Sirui Guo, Yike |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07443 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning Shi, Weijie Zhu, Han Ji, Jiaming Li, Mengze Zhang, Jipeng Zhang, Ruiyuan Zhu, Jia Xu, Jiajie Han, Sirui Guo, Yike Artificial Intelligence 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. |
| title | LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.07443 |