How Far Do Persona Effects Generalize in Language Models?
Persona prompts ask language models to answer as particular kinds of people. We test whether relationships learned from these effects predict responses to new questions and remain useful across models and prompts. Across 57 attributes, three behavioral domains, and seven pairs of open 7 to 9B checkpoints, persona effects can be predictable without being portable. Separate attribute and task gains improve prediction beyond shared scaling significantly in OLMo-3 and Qwen2.5, with the most robust evidence in OLMo-3. In that model, target refitting significantly outperforms gains borrowed from each of the other six pairs. Across model transfers, borrowed gains with one amplitude underperform ...
| Authors | Yufan Zhou, Yuxuan Liu, Enze Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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