Understanding and mitigating the generalization of misalignment
This article investigates how exposing language models to incorrect answers during training can cause misalignment to generalize into broader alignment failures. The researchers identify a specific internal feature responsible for this behavior and demonstrate that targeted fine-tuning with only a small amount of data can correct it, offering insights into LLM alignment mitigation strategies.
We explore how exposing language models to wrong answers during training can trigger wider alignment failures, and we pinpoint an internal feature responsible for this behavior—one that can be corrected with only a small amount of fine-tuning.
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