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

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 INTE...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Kendapadi, Aum, Zaman, Kerem, Menon, Rakesh R, Srivastava, Shashank
Format: Article
Language:English
Subjects:
Image acquisition
Large language models
Questions
Teachers
Online Access:Get full text
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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. 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.
ISSN:2331-8422