Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT

TL;DR

This paper explores how to effectively prompt InstructGPT to generate meaningful analogies and explanations, analyzing prompt design sensitivity and comparing model sizes, with the largest model reaching human-level performance in analogy generation.

Contribution

It systematically studies prompt design for analogy generation with InstructGPT and evaluates the impact of model size and prompt variations on output quality.

Findings

Effective prompts are precise imperative statements.

Model sensitivity varies with prompt phrasing and spelling errors.

Largest InstructGPT model achieves human-level analogy quality.

Abstract

We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG). We found that it is feasible to prompt InstructGPT to generate meaningful analogies and the best prompts tend to be precise imperative statements especially with a low temperature setting. We also systematically analyzed the sensitivity of the InstructGPT model to prompt design, temperature, and injected spelling errors, and found that the model is particularly sensitive to certain variations (e.g., questions vs. imperative statements). Further, we conducted human evaluation on 1.4k of the generated analogies and found that the quality of generations varies substantially by model size. The largest InstructGPT model can achieve human-level performance at generating meaningful analogies for a given target while there is still room for improvement on the AEG task.