Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction

Siyu Yuan, Jiangjie Chen, Xuyang Ge, Yanghua Xiao, Deqing Yang

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Language Modeling and Analysis of Language Models

Keywords: Analogical Reasoning, Large Language Models, Resources and Benchmark

TL;DR: This paper proposes an analogical structure abduction task to evaluate LLMs, which better aligns the analogical reasoning ability of humans.

Abstract: The vital role of analogical reasoning in human cognition allows us to grasp novel concepts by linking them with familiar ones through shared relational structures. Despite the attention previous research has given to word analogies, this work suggests that Large Language Models (LLMs) often overlook the structures that underpin these analogies, raising questions about the efficacy of word analogies as a measure of analogical reasoning skills akin to human cognition. In response to this, our paper introduces a task of analogical structure abduction, grounded in cognitive psychology, designed to abduce structures that form an analogy between two systems. In support of this task, we establish a benchmark called SCAR, containing 400 scientific analogies from 13 distinct fields, tailored for evaluating analogical reasoning with structure abduction. The empirical evidence underlines the continued challenges faced by LLMs, including ChatGPT and GPT-4, in mastering this task, signifying the need for future exploration to enhance their abilities.

Submission Number: 19