A Survey on the Honesty of Large Language Models
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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Siheng Li
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2024-10-01T07:16:49.430Z
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SihengLi
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Siheng99
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Cheng Yang
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Cheng YANG
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Cheng-YANG
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Taiqiang Wu
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2025-05-26T08:45:56.771Z
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Taki WU
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taki555
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Chufan Shi
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Yuji Zhang
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Xinyu Zhu
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2024-10-01T07:16:47.756Z
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Xinyu Zhu
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TianHongZXY
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Zesen Cheng
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2024-10-11T07:44:27.424Z
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Zesen Cheng
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ClownRat
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Deng Cai
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Mo Yu
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2025-02-18T09:36:53.105Z
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Mo
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BishopGorov
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Lemao Liu
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Jie Zhou
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Yujiu Yang
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Ngai Wong
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Xixin Wu
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Wai Lam
A Survey on the Honesty of Large Language Models
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.