The paper introduces OBJECTION, an inference-time pipeline that adds an Adversarial Lawyer Agent to each of the three reasoning steps—offense, unlawfulness, and culpability—in legal judgment prediction models. By actively injecting defense arguments, the agent challenges the model’s default assumption of guilt, which is common in datasets biased toward guilty outcomes. Using a new Natural Innocent dataset of 3.4k real cases, OBJECTION reduces the False Guilty Rate from 82.93% to 16.69%, demonstrating significant improvement in substantive legal reasoning.
By Jaehoon Jeong, Jay-Yoon Lee
LexReward is a taxonomy-driven reward framework designed for legal language models, evaluating responses across three dimensions: Style (lexical and syntactic quality), Element (legal subjects, facts, statutes, and decisions), and Chain (order, completeness, correctness, and non-redundancy of reasoning). The framework introduces rubrics that define evaluation criteria and quality levels for each dimension, generating pairwise preference data used for Direct Preference Optimization (DPO) and reward-model training. Experiments demonstrate that rubric-based rewards effectively differentiate legal responses of varying quality, and that DPO training improves performance across all dimensions; the resulting reward models (LexRM) enable reinforcement learning to enhance policy performance in each specific dimension without needing reference answers.
By Yida Cai, Xin Dai, Bingxiang He, Huiyuan Xie, Yuxiao Ye, Zhenghao Liu, Yang Bai, Zhiyuan Liu
Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-g...
The paper introduces Legal Rule Induction (LRI), a task that seeks to extract concise, generalizable doctrinal rules from analogous judicial precedents. It presents a reproducible pipeline for constructing LRI datasets and, using Chinese law, releases the first benchmark comprising 5,121 case sets (38,088 court cases) for training and 216 expert‑annotated gold test sets. Experiments show that state‑of‑the‑art large language models struggle with over‑generalization and hallucination, but training on the new dataset significantly improves their ability to capture nuanced rule patterns across similar cases.
By Wei Fan, Tianshi Zheng, Yiran Hu, Zheye Deng, Weiqi Wang, Baixuan Xu, Chunyang Li, Haoran Li, Weixing Shen, Yangqiu Song
arXiv:2608. 08830v1 Announce Type: new Abstract: Legal Statute Prediction (LSP) involves automatically identifying relevant legal statutes given factual descriptions in legal documents, typically framed as a multi-label classification task within natural language processing and information retrieval research.
By Subinay Adhikary, Upal Bhattacharya, Vivek Kumar Singh, Anurag Sharma, Shubham Kumar Nigam, Suvasis Das, Shouvik Kumar Guha, Koustav Rudra, Kripabandhu Ghosh
arXiv:2606. 06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing largely to the absence of expert-annotated corpora.
By Xi Xuan, Wenxin Zhang, Yufei Zhou, King-kui Sin, Chunyu Kit