Alignment for Honesty
Recent research has made significant strides in applying alignment techniques
to enhance the helpfulness and harmlessness of large language models (LLMs) in
accordance with human intentions. In this paper, we argue for the importance of
alignment for honesty, ensuring that LLMs proactively refuse to answer
questions when they lack knowledge, while still not being overly conservative.
However, a pivotal aspect of alignment for honesty involves discerning the
limits of an LLM's knowledge, which is far from straightforward. This challenge
demands comprehensive solutions in terms of metric development, benchmark
creation, and training methodologies. In this paper, we address these
challenges by first establishing a precise problem definition and defining
``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone
for developing metrics that effectively measure an LLM's honesty by quantifying
its progress post-alignment. Furthermore, we introduce a flexible training
framework which is further instantiated by several efficient fine-tuning
techniques that emphasize honesty without sacrificing performance on other
tasks. Our extensive experiments reveal that these aligned models show a marked
increase in honesty, as indicated by our proposed metrics. We open-source a
wealth of resources to facilitate future research at
https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned
models, training and evaluation datasets for honesty alignment, concept
glossary, as well as all relevant source code.
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Yuqing Yang
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2025-05-26T08:47:00.382Z
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Yuqing Yang
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Ethan Chern
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2025-04-16T11:38:23.378Z
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Ethan Chern
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Xipeng Qiu
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2023-12-13T09:04:51.713Z
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Xipeng Qiu
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Graham Neubig
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2023-12-13T09:05:03.568Z
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Graham Neubig
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Pengfei Liu
admin_assigned
2023-12-13T09:05:35.416Z
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Pengfei Liu
user
Pengfei
Alignment for Honesty
Recent research has made significant strides in applying alignment techniques
to enhance the helpfulness and harmlessness of large language models (LLMs) in
accordance with human intentions. In this paper, we argue for the importance of
alignment for honesty, ensuring that LLMs proactively refuse to answer
questions when they lack knowledge, while still not being overly conservative.
However, a pivotal aspect of alignment for honesty involves discerning the
limits of an LLM's knowledge, which is far from straightforward. This challenge
demands comprehensive solutions in terms of metric development, benchmark
creation, and training methodologies. In this paper, we address these
challenges by first establishing a precise problem definition and defining
``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone
for developing metrics that effectively measure an LLM's honesty by quantifying
its progress post-alignment. Furthermore, we introduce a flexible training
framework which is further instantiated by several efficient fine-tuning
techniques that emphasize honesty without sacrificing performance on other
tasks. Our extensive experiments reveal that these aligned models show a marked
increase in honesty, as indicated by our proposed metrics. We open-source a
wealth of resources to facilitate future research at
https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned
models, training and evaluation datasets for honesty alignment, concept
glossary, as well as all relevant source code.
A paper proposes a framework and metrics to enhance the honesty of large language models by training them to accurately assess and reveal their knowledge limits.
alignment honesty large language models LLMs fine-tuning