AI RESEARCH
APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings
arXiv CS.CL
•
ArXi:2605.21063v1 Announce Type: new Typical LLM responses tend to follow a default style, even though users often have distinct preferences regarding tone, verbosity, and formality that they do not explicitly state in their prompts. Evaluating whether personalization methods can adapt to these implicit preferences is challenging, since users typically provide prompts rather than reference responses, style preferences are not factually verifiable, and reference-free LLM judges may conflate personalization with general response quality. To address these challenges, we.