title: HoT: Highlighted Chain of Thought for Referencing Supporting Facts from
    Inputs
summary: 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 query. 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. Interestingly, in few-shot settings, HoT outperforms
  vanilla chain of thought prompting (CoT) on a wide range of 17 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 make users believe that an answer is correct.
paper_authors._id: 67c9dc79b918b6494634f899
paper_authors.name: Tin Nguyen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T19:50:18.537Z
paper_authors._id: 639ac72e94cad09c5a16c169
paper_authors.avatarUrl: /avatars/d676019ba2ccb6266791742c5a40a39e.svg
paper_authors.fullname: Tin Nguyen
paper_authors.type: user
paper_authors.user: ngthanhtinqn
paper_authors._id: 67c9dc79b918b6494634f89a
paper_authors.name: Logan Bolton
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:03:30.197Z
paper_authors._id: 668c8e8c142f9b26a49f03cc
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paper_authors.fullname: Logan Bolton
paper_authors.type: user
paper_authors.user: loganbolton
paper_authors._id: 67c9dc79b918b6494634f89b
paper_authors.name: Mohammad Reza Taesiri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-07T09:10:03.139Z
paper_authors._id: 6039478ab3ecf716b1a5fd4d
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paper_authors.fullname: taesiri
paper_authors.type: user
paper_authors.user: taesiri
paper_authors._id: 67c9dc79b918b6494634f89c
paper_authors.name: Anh Totti Nguyen
paper_title: HoT: Highlighted Chain of Thought for Referencing Supporting Facts from
    Inputs
paper_summary: 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 query. 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. Interestingly, in few-shot settings, HoT outperforms
  vanilla chain of thought prompting (CoT) on a wide range of 17 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 make users believe that an answer is correct.
paper_ai_keywords: Large Language Models (LLMs) hallucinate non-factual statements chain-of-thought prompting (CoT) XML tags few-shot settings arithmetic reading comprehension logical reasoning