HumorReject: Decoupling LLM Safety from Refusal Prefix via A Little Humor

ACL ARR 2025 May Submission2188 Authors

Abstract: Large Language Models (LLMs) commonly rely on explicit refusal prefixes for safety, making them vulnerable to prefix injection attacks. We introduce HumorReject, a novel data-driven approach that reimagines LLM safety by decoupling it from refusal prefixes through humor as an indirect refusal strategy. Rather than explicitly rejecting harmful instructions, HumorReject responds with contextually appropriate humor that naturally defuses potentially dangerous requests. Our approach effectively addresses common "over-defense" issues while demonstrating superior robustness against various attack vectors. Our findings suggest that improvements in training data design can be as important as the alignment algorithm itself in achieving effective LLM safety.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: LLM safety, LLM jailbreak

Contribution Types: Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 2188