6 papers · 1 filter
An Explicit Syllogistic Legal Reasoning Framework for Large Language Models
Kepu Zhang, Weijie Yu, Zhongxiang Sun +1
Syllogistic reasoning is crucial for sound legal decision-making, allowing legal professionals to draw logical conclusions by applying general principles to specific case facts. Wh…
Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning
Kepu Zhang, Guofu Xie, Weijie Yu +4
Legal mathematical reasoning is essential for applying large language models (LLMs) in high-stakes legal contexts, where outputs must be both mathematically accurate and procedural…
Trigger: Refining Query Correction via Adaptive Model Selector
Kepu Zhang, Zhongxiang Sun, Xiao Zhang +4
In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines.…
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning
Kepu Zhang, Haoyue Yang, Xu Tang +2
In legal practice, judges apply the trichotomous dogmatics of criminal law, sequentially assessing the elements of the offense, unlawfulness, and culpability to determine whether a…
CitaLaw: Enhancing LLM with Citations in Legal Domain
Kepu Zhang, Weijie Yu, Sunhao Dai +1
In this paper, we propose CitaLaw, the first benchmark designed to evaluate LLMs' ability to produce legally sound responses with appropriate citations. CitaLaw features a diverse…
Effective In-Context Example Selection through Data Compression
Zhongxiang Sun, Kepu Zhang, Haoyu Wang +2
In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingr…