Stanford Paper Tests Per-Prompt Safety Scaling for AI

Rohan Paul posted about a Stanford paper that examines safety alignment in language models. The post notes that standard safety fine-tuning alters behavior on every input, often reducing utility. It states the paper finds that applying scaled safety updates to individual prompts recovers much of the performance lost in global tuning. The tweet describes the core issue as safety weights affecting all prompts regardless of content. Paul identifies the work by its generated headline mentioning CLEAR for utility preservation.