LexPam: Legal Procedure Awareness-Guided Mathematical Reasoning
The legal mathematical reasoning ability of LLMs is crucial when applying them to real-world scenarios, as it directly affects the credibility of the LLM. While existing legal LLMs can perform general judicial question answering, their legal mathematical reasoning capabilities have not been trained. Open-domain reasoning models, though able to generate detailed calculation steps, do not follow the reasoning logic required for legal scenarios. Additionally, there is currently a lack of legal mathematical reasoning datasets to help validate and enhance LLMs' reasoning abilities in legal contexts. To address these issues, we propose the first Chinese legal Mathematical Reasoning Dataset, LexNum, which includes three common legal mathematical reasoning scenarios: economic compensation, work injury compensation, and traffic accident compensation. Based on LexNum, we tested the performance of existing legal LLMs and reasoning LLMs, and introduced LexPam, a reinforcement learning algorithm guided by legal procedural awareness to train LLMs, enhancing their mathematical reasoning abilities in legal scenarios. Experiments on tasks in the three legal scenarios show that the performance of existing legal LLMs and reasoning models in legal mathematical reasoning tasks is unsatisfactory. LexPam can enhance the LLM's ability in these tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Mathematical ReasoningQuestion AnsweringSimilar Papers 제목 키워드 기반
LRAS: Advanced Legal Reasoning with Agentic Search
While Large Reasoning Models (LRMs) have demonstrated exceptional logical capabilities in mathematical domains, their application to the legal field remains hindered by the strict requirements for procedural rigor and ad…
Reinforcement LearningLegal ReasoningA Bayesian Model of the Litigation Game
Over a century ago, Oliver Wendell Holmes invited scholars to look at the law through the lens of probability theory: "The prophecies of what the courts will do in fact, and nothing more pretentious, are what I mean by t…
modelFairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs
Algorithmic fairness has conventionally adopted the mathematically convenient perspective of racial color-blindness (i.e., difference unaware treatment). However, we contend that in a range of important settings, group d…
16kDescriptiveFairnessSelf-directed Machine Learning
Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive, time-con…
BIG-bench Machine LearningModel SelectionLe Nozze di Giustizia. Interactions between Artificial Intelligence, Law, Logic, Language and Computation with some case studies in Traffic Regulations and Health Care
An important aim of this paper is to convey some basics of mathematical logic to the legal community working with Artificial Intelligence. After analysing what AI is, we decide to delimit ourselves to rule-based AI leavi…