The Art of Saying No: Contextual Noncompliance in Language Models

Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, Pradeep Dasigi, Valentina Pyatkin, Abhilasha Ravichander, Sarah Wiegreffe, Nouha Dziri, Khyathi Chandu, Jack Hessel, Yulia Tsvetkov, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi

Keywords: LLM, Safety, Refusal, Noncompliance, Evaluation, Reliable AI

TL;DR: While most existing work primarily focuses on refusal of ``unsafe'' queries, in this work, we posit that noncompliance should extend beyond safety aspects to cover a diverse range of scenarios.

Abstract:

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.

Submission Number: 2136