@inproceedings{karine-marlin-2025-using,
    title = "Using {LLM}s to improve {RL} policies in personalized health adaptive interventions",
    author = "Karine, Karine  and
      Marlin, Benjamin",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.cl4health-1.11/",
    pages = "137--147",
    ISBN = "979-8-89176-238-1",
    abstract = "Reinforcement learning (RL) is increasingly used in the healthcare domain, particularly for the development of personalized adaptive health interventions. However, RL methods are often applied to this domain using small state spaces to mitigate data scarcity. In this paper, we aim to use Large Language Models (LLMs) to incorporate text-based user preferences and constraints, to update the RL policy. The LLM acts as a filter in the action selection. To evaluate our method, we develop a novel simulation environment that generates text-based user preferences and incorporates corresponding constraints that impact behavioral dynamics. We show that our method can take into account the text-based user preferences, while improving the RL policy, thus improving personalization in adaptive intervention."
}