HoT: Highlighted Chain of Thought for Referencing Supporting Facts from
  Inputs
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.
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Tin Nguyen
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Logan Bolton
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Logan Bolton
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Mohammad Reza Taesiri
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Anh Totti Nguyen
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Anh (Totti) Nguyen
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anhng8
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from
  Inputs
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.
Highlighted Chain-of-Thought Prompting improves human verification of LLM responses but can lead to overconfidence in incorrect answers.
Large Language Models Highlighted Chain-of-Thought Prompting XML tags chain of thought prompting few-shot settings arithmetic reading comprehension logical reasoning