用标签标记事实来源，让大模型回答更可信且易验证。
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
- 在输入中添加XML标签，标记关键事实以引导模型引用。
- 在22个任务中降低幻觉率，提升准确率，尤其在逻辑推理上显著改善。
- 适合需要快速验证答案真实性的场景，如医疗或法律咨询。
大型语言模型（LLMs）常生成非事实性陈述，导致人类难以验证并做出正确决策。为此，我们提出带高亮的思维链提示（HoT），通过在输入中加入XML标签，将事实与问题中的关键信息关联。给定一个问题时，模型先重写问题并标注关键事实，再生成带有事实引用标记的回答。相比普通思维链提示（CoT），HoT在超过22项任务（涵盖算术、阅读理解、逻辑推理）中持续降低幻觉率并提升准确性。当人类验证回答时，高亮标记帮助时间有限的参与者更快速、准确地判断模型是否正确。然而，令人意外的是，当模型错误时，这些高亮反而容易误导用户误信其答案为真。
原文摘要 · Abstract (English)
An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate non-factual statements. A response mixed of factual and non-factual statements poses a challenge for humans to verify and accurately base their decisions on. To combat this problem, we propose Highlighted Chain-of-Thought Prompting (HoT), a technique for prompting LLMs to generate responses with XML tags that ground facts to those provided in the question. That is, given an input question, LLMs would first re-format the question to add XML tags highlighting key facts, and then, generate a response with highlights over the facts referenced from the input. Compared to vanilla chain of thought prompting (CoT), HoT reduces the rate of hallucination and separately improves LLM accuracy consistently on over 22 tasks from arithmetic, reading comprehension, to logical reasoning. When asking humans to verify LLM responses, highlights help time-limited participants to more accurately and efficiently recognize when LLMs are correct. Yet, surprisingly, when LLMs are wrong, HoTs tend to fool users into believing that an answer is correct.
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