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Oct 12 · Monday
Defenses and guardrails · Papers Found 3 papers “
About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
解释后验安全的权重几何脆弱性，并提出预训练持续安全共训练
Published Sep 7
Evaluated models Qwen-2.5-7B , Qwen-2.5-7B-Instruct , Llama-3-8B and others15 Abstract 论文将后验安全脆弱性归因于正交抑制，并提出通过预训练持续共训练实现具备微调抗性的安全防护。
Proposes Rationale-Only safety fine-tuning, removing the refusal statement to reduce false refusals.
Published Sep 4
Evaluated models Llama-3.1-8B , Mistral-7B-v0.3 , Gemma-2-9B and others8 Abstract Removing the boilerplate refusal statement and training only on the rationale can reduce false refusals, while the compliance rate on harmful queries remains low.This is a structural analysis of safety fine-tuning data: false refusals are traced by the authors to formulaic refusal statements in responses, rather than relying only on inference-time remedies.
提出 DataShield，用共识子空间过滤会削弱安全的微调数据
Published Jul 16
Evaluated models Phi3-medium-4k-it , Qwen3-4B-it , Gemma2-27B-it and others4 You have reached the end