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Oct 12 · Monday
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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.
提出 MASCRDM，在监督微调中实时检测并缓解偏见
Published Sep 30
Evaluated models Qwen3-8B-Base , Llama-3.1-8B Abstract MASCRDM 在 SFT 中按法规图谱检测数据、结构与损失风险，BBQ 总准确率在两模型上最高。MASCRDM 把 AI 法律规章编成知识图谱，再由五个智能体在 LLM 训练中做实时合规检测与缓解，实验以偏见与歧视为代表风险。
提出可解释 rubric 奖励，将宪法转为可调权重并用于监督微调
Published Sep 27
Evaluated models Gemma 3 12B , Qwen3 14B , GPT-OSS 20B Abstract 宪法级可解释奖励让优先级可检查、可改写，并带入训练后的行为。作者给出一条从通用宪法到可调训练奖励的路径，用来检查并改写模型实际被优化的优先级。
分析按 Schwartz 价值对齐后的意外危害，并提出上下文对齐方法
Published Jun 6, 2025
Evaluated models Llama-2 7B , Llama-3.1 8B , Gemma-2 9B and others6 Abstract 价值对齐会随 Schwartz 价值改变有害倾向。作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害，并用提示做了一个不直说要安全的缓解试验。
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