title: A Survey on the Honesty of Large Language Models
summary: Honesty is a fundamental principle for aligning large language models (LLMs)
  with human values, requiring these models to recognize what they know and don't
  know and be able to faithfully express their knowledge. Despite promising,
  current LLMs still exhibit significant dishonest behaviors, such as confidently
  presenting wrong answers or failing to express what they know. In addition,
  research on the honesty of LLMs also faces challenges, including varying
  definitions of honesty, difficulties in distinguishing between known and
  unknown knowledge, and a lack of comprehensive understanding of related
  research. To address these issues, we provide a survey on the honesty of LLMs,
  covering its clarification, evaluation approaches, and strategies for
  improvement. Moreover, we offer insights for future research, aiming to inspire
  further exploration in this important area.
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paper_authors.name: Cheng Yang
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paper_authors.name: Taiqiang Wu
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paper_authors.name: Chufan Shi
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paper_authors.name: Yuji Zhang
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paper_authors.fullname: Xinyu Zhu
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paper_authors.name: Zesen Cheng
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paper_authors.fullname: Zesen Cheng
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paper_authors.user: ClownRat
paper_authors._id: 66facd02b505f1a04c28cf30
paper_authors.name: Deng Cai
paper_authors._id: 66facd02b505f1a04c28cf31
paper_authors.name: Mo Yu
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paper_authors.fullname: Mo
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paper_authors.user: BishopGorov
paper_authors._id: 66facd02b505f1a04c28cf32
paper_authors.name: Lemao Liu
paper_authors._id: 66facd02b505f1a04c28cf33
paper_authors.name: Jie Zhou
paper_authors._id: 66facd02b505f1a04c28cf34
paper_authors.name: Yujiu Yang
paper_authors._id: 66facd02b505f1a04c28cf35
paper_authors.name: Ngai Wong
paper_authors._id: 66facd02b505f1a04c28cf36
paper_authors.name: Xixin Wu
paper_authors._id: 66facd02b505f1a04c28cf37
paper_authors.name: Wai Lam
paper_title: A Survey on the Honesty of Large Language Models
paper_summary: Honesty is a fundamental principle for aligning large language models (LLMs)
  with human values, requiring these models to recognize what they know and don't
  know and be able to faithfully express their knowledge. Despite promising,
  current LLMs still exhibit significant dishonest behaviors, such as confidently
  presenting wrong answers or failing to express what they know. In addition,
  research on the honesty of LLMs also faces challenges, including varying
  definitions of honesty, difficulties in distinguishing between known and
  unknown knowledge, and a lack of comprehensive understanding of related
  research. To address these issues, we provide a survey on the honesty of LLMs,
  covering its clarification, evaluation approaches, and strategies for
  improvement. Moreover, we offer insights for future research, aiming to inspire
  further exploration in this important area.