Aligned Data Can Induce Misalignment via Context Confusion

The paper reports that fine‑tuning large language models on aligned data can unintentionally cause misaligned responses in other contexts—a phenomenon termed *context confusion*. The authors demonstrate this effect in gender equality, privacy, and physical safety domains, showing that it differs from emergent misalignment and is not mitigated by general alignment data but can be reduced with domain‑specific alignment or in‑context examples. They provide a mechanistic explanation based on representational shifts during fine‑tuning that lead to behavioral feature transfer across contexts.

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• llms
• fine-tuning
• safety