The Refusal--Compliance Tradeoff: A Large-Scale Safety Behavior Audit of Large Language Models
Refusal rates are a poor proxy for LLM safety, i.e., a model may over-refuse benign prompts while still complying with harmful ones. We audit both failure modes across 21 open-weight LLMs on four safety benchmarks (OR-Bench, XSTest, ToxiGen, BOLD), using a composition adjustment to isolate model sensitivity from dataset toxicity confounds. We report three findings. First, models adopt fundamentally different calibration strategies: conservative ecosystems such as Llama suppress unsafe outputs at the cost of elevated over-refusals, while permissive ecosystems such as DeepSeek and Qwen preserve helpfulness but tolerate higher harmful compliance. Second, demographic protection is unequal: mo...
| Authors | Alif Al Hasan, Sumon Biswas |
|---|---|
| Primary category | cs.AI |
| Published | 6 May 2026 |
| Last updated at source | 30 May 2026 |
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