Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling

Excerpt

arXiv:2607.20791v2 Announce Type: replace-cross Abstract: 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 mo