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arXiv · 2501.06521

Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering

Yinghao Hu·Leilei Gan·Wenyi Xiao·Kun Kuang·Fei Wu
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Abstract

Hallucination, or the generation of incorrect or fabricated information, remains a critical challenge in large language models (LLMs), particularly in high-stake domains such as legal question answering (QA). In order to mitigate the hallucination rate in legal QA, we first introduce a benchmark called LegalHalBench and three automatic metrics to evaluate the common hallucinations when LLMs answer legal questions. We then propose a hallucination mitigation method that integrates behavior cloning and a novel Hard Sample-aware Iterative Direct Preference Optimization (HIPO). We conduct extensive real-data experiments to validate the effectiveness of our approach. Our results demonstrate remarkable improvements in various metrics, including the newly proposed Non-Hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, as well as traditional metrics such as METEOR, BERTScore, ROUGE-L, and win rates.

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Yinghao Hu, Leilei Gan, Wenyi Xiao, Kun Kuang, Fei Wu. 2025-01-11. Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering. https://arxiv.org/abs/2501.06521

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