mlsanityData to 14 Oct 2026

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

arXiv 2412.11388 ↗First posted 16 Dec 2024cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

21 of 47 references.

References · 21

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  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
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9 more
  1. 2020doc2dial: A Goal-Oriented Document-Grounded Dialogue Datasetcited by 88
  2. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
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  4. 2021MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documentscited by 53
  5. 2024Bayesian Preference Elicitation with Language Modelscited by 34
  6. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
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  9. 2024Privacy for Free in the Overparameterized Regimecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Upcited by 6
  2. 2026Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Predictioncited by 1
  3. 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
  4. 2025Socratic Students: Teaching Language Models to Learn by Asking Questionscited by 0
  5. 2026When to Ask a Question: Understanding Communication Strategies in Generative AI Toolscited by 0
  6. 2026DICE: Decoupling Capability from Intervention Necessity in LLM Tutoringcited by 0