Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization

摘要

Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of \textbf{factual hallucinations}, which can be difficult to detect and mislead users without relevant knowledge. Although post-training techniques have been employed to mitigate the issue, existing methods usually suffer from poor generalization and trade-offs in other different capabilities. In this paper, we propose to address these by directly augmenting LLM’s fundamental ability to precisely leverage its knowledge and introduce \textbf{PKUE} (\textbf{P}recise \textbf{K}nowledge \textbf{U}tilization \textbf{E}nhancement), which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization. Furthermore, we construct \textbf{FactualBench}, a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains, to facilitate both evaluation and training. Extensive experiments demonstrate that PKUE significantly improves LLM overall performance, with consistent enhancement across factual tasks of various forms, general tasks beyond factuality, and tasks in different language.