TamSiuhin/LLM-UM-Reading
A list of large language models for user modeling (LLM-UM) papers, based on "User Modeling in the Era of Large Language Models: Current Research and Future Directions" at DEBULL
This reading list helps researchers and practitioners understand how Large Language Models (LLMs) are being used to analyze and predict user behavior. It compiles academic papers that explore using LLMs to interpret user-generated content and historical data, resulting in insights for tasks like personalizing recommendations, building user profiles, or detecting problematic content. The primary audience for this resource is researchers in user modeling, recommender systems, or computational social science.
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Use this if you are an academic or industry researcher interested in the state-of-the-art applications of large language models for understanding and predicting user characteristics and actions.
Not ideal if you are looking for ready-to-use LLM code or a non-technical introduction to artificial intelligence.
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Apr 08, 2024
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