HoT: 用于从输入中引用支持事实的高亮推理链
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
AI总结：
本文提出HoT技术，通过XML标签高亮输入中的关键事实，减少LLM的幻觉问题，提升22项任务的准确性，并帮助人类更高效地验证结果。
摘要：
大型语言模型（LLM）的一个弱点是倾向于生成非事实性陈述。混合事实和非事实性陈述的响应对人类验证和基于输入的决策构成挑战。为解决此问题，我们提出高亮推理链提示（HoT），一种通过XML标签将事实与问题中提供的事实联系起来的提示技术。即，给定一个输入问题，LLM会首先重新格式化问题以添加XML标签突出关键事实，然后生成带有高亮引用输入事实的响应。与传统的推理链提示（CoT）相比，HoT减少了幻觉率，并在超过22项任务上一致提高了LLM的准确性。当要求人类验证LLM响应时，高亮帮助限时参与者更准确高效地识别LLM正确性。然而，令人惊讶的是，当LLM错误时，HoT倾向于使用户误认为答案正确。
英文摘要：
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.
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。
