CAM: A Large Language Model-based Creative Analogy Mining Framework

Bhavya Bhavya, Jinjun Xiong, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Analogies inspire creative solutions to problems, and facilitate the creative expression of ideas and the explanation of complex concepts. They have widespread applications in scientific innovation, creative writing, and education. The ability to discover creative analogies that are not explicitly mentioned but can be inferred from the web is highly desirable to power all such applications dynamically and augment human creativity. Recently, Large Pre-trained Language Models (PLMs), trained on massive Web data, have shown great promise in generating mostly known analogies that are explicitly mentioned on the Web. However, it is unclear how they could be leveraged for mining creative analogies not explicitly mentioned on the Web. We address this challenge and propose Creative Analogy Mining (CAM), a novel framework for mining creative analogies, which consists of the following three main steps: 1) Generate analogies using PLMs with effectively designed prompts, 2) Evaluate their quality using scoring functions, and 3) Refine the low-quality analogies by another round of prompt-based generation. We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. Based on human evaluation using Amazon Mechanical Turk, we find that our unsupervised framework can mine 13.7% highly-creative and 56.37% somewhat-creative analogies. Moreover, our supervised scores are generally better than the unsupervised ones and correlate moderately with human evaluators, indicating that they would be even more effective at mining creative analogies. These findings also shed light on the creativity of PLMs 1.

Original languageEnglish (US)
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)9781450394161
StatePublished - Apr 30 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: Apr 30 2023May 4 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023


Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States


  • analogy mining
  • creativity
  • large language model

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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