Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling
Recent advances in truncation-based sampling have helped mitigate drawbacks of high-temperature sampling such as neural text degeneration, thereby enabling greater diversity without sacrificing coherence. However, increasing the entropy of the token probability distribution via high temperatures has also been shown to weaken the model's refusal response. Existing solutions for maintaining the refusal behavior of LLMs either replace the model's own refusal decision with a separate safety classifier or alter its output distribution for every prompt. To address this gap, we propose refusal-gated decoding (RGD): an efficient sequential decoding approach which preserves a model's greedy decodi...
| Authors | Phillip Howard, Xin Su, Allen Roush et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 5 October 2026 |
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