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This paper proposes PUMA, a framework for LLM personalization in multi-turn conversations that models latent user states and uses the Free Energy Principle to select dialogue actions, improving long-horizon outcomes on healthcare counseling benchmarks.
A new paper by Microsoft Research and Salesforce reveals that LLM performance drops significantly in multi-turn conversations due to a 'Lost in Conversation' phenomenon, challenging the reliability of current single-turn benchmarks.