Introduces APM benchmark with hidden randomized mappings to evaluate LLM style personalization methods fairly, finding routing most reliable while overall personalization remains challenging.
Proper: A progressive learning framework for personalized large language models with group-level adaptation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings
Introduces APM benchmark with hidden randomized mappings to evaluate LLM style personalization methods fairly, finding routing most reliable while overall personalization remains challenging.