Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
This work addresses the problem of automating analogy generation for natural language processing applications, though it is incremental as it applies existing methods to a new task.
The study explored prompting InstructGPT to generate analogies, finding that precise imperative prompts with low temperature yield meaningful results, and the largest model achieved human-level performance on generating analogous concepts but lagged on explanations.
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.