title: The Art of Saying No: Contextual Noncompliance in Language Models
summary: Chat-based language models are designed to be helpful, yet they should not
  comply with every user request. While most existing work primarily focuses on
  refusal of "unsafe" queries, we posit that the scope of noncompliance should be
  broadened. We introduce a comprehensive taxonomy of contextual noncompliance
  describing when and how models should not comply with user requests. Our
  taxonomy spans a wide range of categories including incomplete, unsupported,
  indeterminate, and humanizing requests (in addition to unsafe requests). To
  test noncompliance capabilities of language models, we use this taxonomy to
  develop a new evaluation suite of 1000 noncompliance prompts. We find that most
  existing models show significantly high compliance rates in certain previously
  understudied categories with models like GPT-4 incorrectly complying with as
  many as 30% of requests. To address these gaps, we explore different training
  strategies using a synthetically-generated training set of requests and
  expected noncompliant responses. Our experiments demonstrate that while direct
  finetuning of instruction-tuned models can lead to both over-refusal and a
  decline in general capabilities, using parameter efficient methods like low
  rank adapters helps to strike a good balance between appropriate noncompliance
  and other capabilities.
paper_authors._id: 669883d63fea9b7c13086ca0
paper_authors.name: Faeze Brahman
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paper_authors.name: Sachin Kumar
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paper_authors.fullname: Sachin Kumar
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paper_authors.name: Vidhisha Balachandran
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paper_authors.name: Pradeep Dasigi
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paper_authors.name: Valentina Pyatkin
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paper_authors.fullname: Valentina Pyatkin
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paper_authors.user: valpy
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paper_authors.name: Abhilasha Ravichander
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paper_authors.fullname: Abhilasha Ravichander
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paper_authors.user: lasha-nlp
paper_authors._id: 669883d63fea9b7c13086ca6
paper_authors.name: Sarah Wiegreffe
paper_authors._id: 669883d63fea9b7c13086ca7
paper_authors.name: Nouha Dziri
paper_authors._id: 669883d63fea9b7c13086ca8
paper_authors.name: Khyathi Chandu
paper_authors._id: 669883d63fea9b7c13086ca9
paper_authors.name: Jack Hessel
paper_authors._id: 669883d63fea9b7c13086caa
paper_authors.name: Yulia Tsvetkov
paper_authors._id: 669883d63fea9b7c13086cab
paper_authors.name: Noah A. Smith
paper_authors._id: 669883d63fea9b7c13086cac
paper_authors.name: Yejin Choi
paper_authors._id: 669883d63fea9b7c13086cad
paper_authors.name: Hannaneh Hajishirzi
paper_title: The Art of Saying No: Contextual Noncompliance in Language Models
paper_summary: Chat-based language models are designed to be helpful, yet they should not
  comply with every user request. While most existing work primarily focuses on
  refusal of "unsafe" queries, we posit that the scope of noncompliance should be
  broadened. We introduce a comprehensive taxonomy of contextual noncompliance
  describing when and how models should not comply with user requests. Our
  taxonomy spans a wide range of categories including incomplete, unsupported,
  indeterminate, and humanizing requests (in addition to unsafe requests). To
  test noncompliance capabilities of language models, we use this taxonomy to
  develop a new evaluation suite of 1000 noncompliance prompts. We find that most
  existing models show significantly high compliance rates in certain previously
  understudied categories with models like GPT-4 incorrectly complying with as
  many as 30% of requests. To address these gaps, we explore different training
  strategies using a synthetically-generated training set of requests and
  expected noncompliant responses. Our experiments demonstrate that while direct
  finetuning of instruction-tuned models can lead to both over-refusal and a
  decline in general capabilities, using parameter efficient methods like low
  rank adapters helps to strike a good balance between appropriate noncompliance
  and other capabilities.
paper_ai_keywords: contextual noncompliance taxonomy noncompliance suite prompt compliance rates instruction-tuned models direct finetuning parameter-efficient methods low rank adapters