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Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?

Siyu Yuan, Cheng Jiayang, Lin Qiu, Deqing Yang

TL;DR

This paper proposes to investigate how analogies created by teacher language models (LMs) can assist student LMs in understanding scientific concepts, thereby aligning more closely with practical scenarios and suggests that free-form analogies can indeed aid LMs in understanding concepts.

Abstract

Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones. Previous research in the AI community has mainly focused on identifying and generating analogies and then examining their quality under human evaluation, which overlooks the practical application of these analogies in real-world settings. Inspired by the human education process, in this paper, we propose to investigate how analogies created by teacher language models (LMs) can assist student LMs in understanding scientific concepts, thereby aligning more closely with practical scenarios. Our results suggest that free-form analogies can indeed aid LMs in understanding concepts. Additionally, analogies generated by student LMs can improve their own performance on scientific question answering, demonstrating their capability to use analogies for self-learning new knowledge. Resources are available at https://github.com/siyuyuan/SCUA.

Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?

TL;DR

This paper proposes to investigate how analogies created by teacher language models (LMs) can assist student LMs in understanding scientific concepts, thereby aligning more closely with practical scenarios and suggests that free-form analogies can indeed aid LMs in understanding concepts.

Abstract

Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones. Previous research in the AI community has mainly focused on identifying and generating analogies and then examining their quality under human evaluation, which overlooks the practical application of these analogies in real-world settings. Inspired by the human education process, in this paper, we propose to investigate how analogies created by teacher language models (LMs) can assist student LMs in understanding scientific concepts, thereby aligning more closely with practical scenarios. Our results suggest that free-form analogies can indeed aid LMs in understanding concepts. Additionally, analogies generated by student LMs can improve their own performance on scientific question answering, demonstrating their capability to use analogies for self-learning new knowledge. Resources are available at https://github.com/siyuyuan/SCUA.
Paper Structure (29 sections, 4 figures, 4 tables)

This paper contains 29 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Analogical Reasoning
  4. Explanation Generation
  5. SCUA Task
  6. Task Formulation
  7. Analogy Generation from Teacher LMs
  8. Scientific Concept Extraction
  9. Analogy Type
  10. Scientific QA for Student LMs
  11. Experiment
  12. Evaluation Protocol
  13. Evaluation Models
  14. Evaluation Collection
  15. Evaluation Metrics
  16. ...and 14 more sections

Figures (4)

  • Figure 1: An example of the SCUA task. Given a scientific concept (i.e., Atom), we ask teacher LMs to generate an analogy to explain the concept and then let student LMs answer the related scientific questions around this concept, both with and without the aid of the generated analogy.
  • Figure 2: The performance of different student LMs under different types of analogies generated by GPT-4.
  • Figure 3: The screenshots of the instructions and interface for extracted concept annotation.
  • Figure 4: The screenshots of the instructions and interface for generated analogy annotation.