LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning
Weijie Shi, Han Zhu, Jiaming Ji, Mengze Li, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Sirui Han, Yike Guo
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 Legal-Reasoner 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 .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1a08cb40-395c-4dd0-9af4-499e9f8242adCited by top-tier papers7
- PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal PracticeYuzhen Shi, Huanghai Liu, Yiran Hu, Gaojie Song et al.ACL 2026 · 7 citations
- Reimagining Legal Fact Verification with GenAI: Toward Effective Human-AI CollaborationSirui Han, Yuyao Zhang, Yidan Huang, Xueyan Li et al.CHI 2026 · 3 citations
- What, Whether and How? Unveiling Process Reward Models for Thinking with Images ReasoningYujin Zhou, Pengcheng Wen, Jiale Chen, Boqin Yin et al.AAAI 2026 · 2 citations
- Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi, Ilaria Bartolini et al.ACL 2026 · 1 citation
- Benchmarking Fine-Grained Error Detection in Multimodal ReasoningChi-Min Chan, Han Zhu, Chunyang Jiang, Jiaming Ji et al.ACL 2026
Builds on6
- Self-Refine: Iterative Refinement with Self-FeedbackAman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan et al.NeurIPS 2023 · 4,972 citations
- CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingZhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen et al.ICLR 2024 · 699 citations
- Self-Consistency Improves Chain of Thought Reasoning in Language ModelsXuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le et al.ICLR 2023 · 681 citations
- ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree SearchDan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue et al.NeurIPS 2024 · 527 citations
- ML-LJP: Multi-Law Aware Legal Judgment PredictionYifei Liu, Yiquan Wu, Yating Zhang, Changlong Sun et al.SIGIR 2023 · 31 citations
Related papers
- Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction FrameworkYiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang et al.EMNLP 2022 · 33 citations
- Evaluating Legal Reasoning Traces with Legal Issue Tree RubricsJinu Lee, Kyoung-Woon On, Sophia Simeng Han, Arman Cohan et al.ACL 2026 · 2 citations
- ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for Mllm-Based Process JudgesJiaxin Ai, Pengfei Zhou, Zhaopan Xu, Ming Li et al.ICCV 2025 · 9 citations
- CourtReasoner: Can LLM Agents Reason Like Judges?Sophia Simeng Han, Yoshiki Takashima, Shannon Zejiang Shen, Chen Liu et al.EMNLP 2025 · 1 citation
- LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal ReasoningZerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei et al.ACL 2026 · 2 citations
