See it, Say it, Sorted: Mechanistic Diagnosis and Parameter-Space Mitigation of Emergent Misalignment in LLMs
A geometric mitigation framework cuts emergent misalignment on Qwen2.5-14B-IT by up to 80%.
The paper studies emergent misalignment, in which narrow domain adaptation causes catastrophic safety failures in unrelated domains. Training-trajectory analysis shows directional Hessian curvature concentrates on semantic pivot tokens, and the harmful-safe gap widens mainly because safe-gradient overlap declines. A Geometric Mitigation Framework orthogonally projects the empirical harmful gradient subspace out of parameter updates. On Qwen2.5-14B-IT it suppresses free-generation emergent misalignment by up to 80%; across other open-weight instruction models from 3B to 20B, the same subspace still controls conditional support of harmful responses even when observed behavioral misalignment is near zero.
- Narrow adaptation can trigger safety failures in unrelated domains.
- Harmful gradient subspace is projected out of parameter updates.
- Free-generation emergent misalignment drops by up to 80% on Qwen2.5-14B-IT.
- The same subspace remains steerable when behavioral EM looks near zero.
- Organizations
- Alibaba
- AI models
- Qwen2.5-14B-Instruct
From the original article
1 min read at huggingface.co
Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated domains. Prior static analyses leave training dynamics unmapped, while existing defenses rely on heuristics that degrade utility. We present a dynamic, second-order geometric study of EM.
By Weiqiao Que, Ruizhe Li, Chengyu Wang · huggingface.co
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