INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models

• URL: http://arxiv.org/abs/2412.11388v1
• Date: Mon, 16 Dec 2024 02:28:53 GMT
• Title: INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models
• Authors: Aum Kendapadi, Kerem Zaman, Rakesh R. Menon, Shashank Srivastava,
• Abstract summary: 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.
• Score: 15.825663946923289
• License:
• 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 (INTEReractive 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.

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