Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs
Personalized LLMs must decide, for each stored preference, whether the current context calls for applying or suppressing it, which we call its applicability. They frequently over-personalize, applying preferences the context rules out, yet existing benchmarks score only the final response and cannot tell where this failure arises. We decompose preference handling into three stages and measure each separately: (1) knowing whether a preference applies, (2) deciding on an explicit Apply/Suppress label, and (3) generating a response consistent with that label. Using linear probes, we first show that this applicability signal remains decodable from hidden states during generation. By making th...
| Authors | Haeun Jang, Yonghyun Jun, Hwanhee Lee |
|---|---|
| Primary category | cs.CL |
| Published | 28 September 2026 |
| Last updated at source | 30 September 2026 |
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