LLM Personalization Systems Fail on Real Human Data Across All Three Stages

A study collecting 550 real human conversations and nearly 19,000 human judgments finds LLM personalization systems underperform at attribute extraction, relevance matching, and response generation compared to synthetic-data evaluations. Models disagree with human judgments on which attributes are relevant, exposing a systematic gap between benchmark and real-world performance.