@inproceedings{kendapadi-etal-2025-interact,
    title = "{INTERACT}: Enabling Interactive, Question-Driven Learning in Large Language Models",
    author = "Kendapadi, Aum  and
      Zaman, Kerem  and
      R Menon, Rakesh  and
      Srivastava, Shashank",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.441/",
    pages = "8992--9024",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) excel at answering questions but remain passive learners{---}absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTERACT (INTERactive learning for Adaptive Concept Transfer), a framework in which a ``student'' LLM engages a ``teacher'' LLM through iterative inquiries to acquire knowledge across 1,347 contexts, including song lyrics, news articles, movie plots, academic papers, and images. Our experiments show that across a wide range of scenarios and LLM architectures, interactive learning consistently enhances performance, achieving up to a 25{\%} improvement, with `cold-start' student models matching static learning baselines in as few as five dialogue turns. Interactive setups can also mitigate the disadvantages of weaker teachers, showcasing the robustness of question-driven learning."
}