INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915316393574400 |
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| author | Kendapadi, Aum Zaman, Kerem Menon, Rakesh R. Srivastava, Shashank |
| author_facet | Kendapadi, Aum Zaman, Kerem Menon, Rakesh R. Srivastava, Shashank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11388 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models Kendapadi, Aum Zaman, Kerem Menon, Rakesh R. Srivastava, Shashank Computation and Language 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. |
| title | INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.11388 |