Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks \u2013 from offering writing support to delivering tailored recommendations or consultations. Over time, the interaction history between a user and an LLM can provide extensive information about an individual\u2019s traits and preferences. However, open questions remain on how well LLMs today can effectively leverage such history to (1) internalize the user\u2019s inherent traits and preferences, (2) track how the user profiling and preferences evolve over time, and (3) generate personalized responses accordingly in new scenarios.<\/p>\n
In this work, we introduce the PERSONAMEM benchmark. PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories, each containing up to 60 sessions of multi-turn conversations across 15 real-world tasks that require personalization. Given an in-situ user query at a specific time point, we evaluate LLM chatbots\u2019 ability to identify the most suitable response according to the current state of the user\u2019s profile. We observe that current LLMs still struggle to recognize the dynamic evolution in users\u2019 profiles over time through direct prompting approaches. As a consequence, LLMs often fail to deliver responses that align with users\u2019 current situations and preferences, with frontier models such as GPT-4.5, or Gemini-2.0 achieving only around 50% overall accuracy, suggesting room for improvement. We hope that PERSONAMEM, along with the user profile and conversation simulation pipeline, can facilitate future research in the development of truly user-aware chatbots.<\/p>\n","protected":false},"excerpt":{"rendered":"
Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks \u2013 from offering writing support to delivering tailored recommendations or consultations. Over time, the interaction history between a user and an LLM can provide extensive information about an individual\u2019s traits and preferences. 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