用语义熵指导大模型学会在不确定时拒绝回答，减少幻觉。
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy
- 基于模型自省的语义熵衡量不确定性，无需外部标签。
- 在短长文本生成上均优于已有方法，显著降低幻觉率。
- 适合医疗、法律等高风险场景，提升模型可信度。
大语言模型常产生看似合理实则错误的文本（幻觉），在医疗、法律等关键领域带来风险，亟需有效缓解策略。现有方法依赖真实标签或仅适用于短文本回复，存在局限。本文提出基于语义熵的微调方法，通过模型自身推理过程评估不确定性，无需外部标注。实验表明，该方法在多种数据集上对短文本和长文本生成均表现优异，性能不低于甚至超过已有先进方法，有效抑制幻觉。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are known to hallucinate, whereby they generate plausible but inaccurate text. This phenomenon poses significant risks in critical applications, such as medicine or law, necessitating robust hallucination mitigation strategies. While recent works have proposed fine-tuning methods to teach LLMs to abstain from answering questions beyond their knowledge or capabilities, these methods rely on the existence of ground-truth labels or are limited to short-form responses. To address these limitations, we propose fine-tuning using semantic entropy, an uncertainty measure derived from introspection into the model which does not require external labels. We demonstrate that our approach matches or outperforms models fine-tuned using prior work and achieves strong performance for both short and long-form generations on a range of datasets.
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