Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation

MCML Authors

Xinpeng Wang

→ Group Barbara Plank
 AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

Abstract

Training a language model to be both helpful and harmless requires careful calibration of refusal behaviours: Models should refuse to follow malicious instructions or give harmful advice (e.g.'how do I kill someone?'), but they should not refuse safe requests, even if they superficially resemble unsafe ones (e.g. 'how do I kill a Python process?'). Avoiding such false refusal, as prior work has shown, is challenging even for highly-capable language models. In this paper, we propose a simple and surgical method for mitigating false refusal in language models via single vector ablation. For a given model, we extract a false refusal vector and show that ablating this vector reduces false refusal rate while preserving the model's safety and general capabilities. We also show that our approach can be used for fine-grained calibration of model safety. Our approach is training-free and model-agnostic, making it useful for mitigating the problem of false refusal in current and future language models.

inproceedings

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ICLR 2025

13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025.

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Authors

X. Wang • C. Hu • P. Röttger • B. Plank

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Links

URL

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Research Area

 B2 | Natural Language Processing