title: Alignment for Honesty
summary: 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.
paper_authors._id: 65791cc9d54197901f840ded
paper_authors.name: Yuqing Yang
paper_authors._id: 65791cc9d54197901f840dee
paper_authors.name: Ethan Chern
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paper_authors.fullname: Ethan Chern
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paper_authors.user: ethanchern
paper_authors._id: 65791cc9d54197901f840def
paper_authors.name: Xipeng Qiu
paper_authors.status: admin_assigned
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paper_authors.fullname: Xipeng Qiu
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paper_authors.user: xpqiu
paper_authors._id: 65791cc9d54197901f840df0
paper_authors.name: Graham Neubig
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paper_authors.fullname: Graham Neubig
paper_authors.type: user
paper_authors.user: gneubig
paper_authors._id: 65791cc9d54197901f840df1
paper_authors.name: Pengfei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-13T09:05:35.416Z
paper_authors._id: 6144a0c4ff1146bbd84d9865
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paper_authors.fullname: Pengfei Liu
paper_authors.type: user
paper_authors.user: Pengfei
paper_title: Alignment for Honesty
paper_summary: 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.
paper_ai_keywords: alignment techniques large language models (LLMs) honesty knowledge limits metric development benchmark creation training methodologies problem definition fine-tuning techniques performance on other tasks honest models