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

• URL: http://arxiv.org/abs/2501.06521v1
• Date: Sat, 11 Jan 2025 12:08:15 GMT
• Title: Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering
• Authors: Yinghao Hu, Leilei Gan, Wenyi Xiao, Kun Kuang, Fei Wu,
• Abstract summary: Hallucination, or the generation of incorrect or fabricated information, remains a critical challenge in large language models. We propose a hallucination mitigation method that integrates behavior cloning and a novel Hard Sample-aware Iterative Direct Preference Optimization (HIPO) Our results demonstrate remarkable improvements in various metrics, including the newly proposed Non-Hallucinated Statute Rate.
• Score: 30.795942355722797
• License:
• 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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