探索通过增强精确知识利用来缓解事实性幻觉的泛化能力
Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization
AI总结：
本文提出PKUE方法，通过偏好优化微调模型精确利用知识的能力，并构建FactualBench数据集，显著提升了LLM在事实性、通用及多语言任务上的整体性能。
摘要：
大型语言模型（LLMs）通常难以使其响应与客观事实保持一致，从而导致事实幻觉问题，该问题难以检测且会误导缺乏相关知识的用户。尽管已采用后训练技术来缓解此问题，但现有方法通常泛化能力差，并在其他不同能力上存在权衡。本文中，我们提出通过直接增强LLM精确利用其知识的基础能力来解决这些问题，并引入了PKUE（Precise Knowledge Utilization Enhancement，精确知识利用增强），该方法通过偏好优化对模型在针对精确且简单的事实问题的自生成响应上进行微调。此外，我们构建了FactualBench，这是一个全面且精确的事实问答数据集，包含跨越21个领域的181k中文数据，以促进评估和训练。大量实验表明，PKUE显著提升了LLM的整体性能，在各种形式的事实任务、超越事实性的通用任务以及不同语言的任务中均表现出一致的增强。
英文摘要：
Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of 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 PKUE (Precise Knowledge Utilization Enhancement), which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization. Furthermore, we construct 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
