@inproceedings{wu-etal-2026-systematic,
title = "A Systematic Review of Analogy Generation and Evaluation: Methods, Metrics, and Challenges",
author = "Wu, Shutong and
Zhang, Tianyu and
Wang, Hecong and
Lu, Yang and
Yang, Jiankun and
Tang, Jiamu and
Bai, Zhen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.463/",
pages = "9710--9734",
ISBN = "979-8-89176-462-0",
abstract = "Analogy is a quintessential human cognitive capability that supports knowledge transfer across domains. While researchers have long studied analogy generation, evaluation, and reasoning through symbolic and connectionist approaches, the rise of large language models (LLMs) has introduced new generation systems and evaluation methods. However, a systematic understanding of this emerging landscape and its connection to prior work remains underexplored. To address this gap, we conduct a PRISMA-based systematic literature review spanning four decades across six databases. We identify three key observations: first, generation and evaluation methods are strongly shaped by analogy granularity, such as lexical versus compositional analogies; second, human-in-the-loop design plays an important role in LLM-based analogy generation systems; and third, the field remains divided over whether LLMs perform genuine analogical reasoning. Based on our review, we propose practical guidelines for future work on analogy generation, including potential evaluation dimensions for lexical and compositional tasks."
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%0 Conference Proceedings
%T A Systematic Review of Analogy Generation and Evaluation: Methods, Metrics, and Challenges
%A Wu, Shutong
%A Zhang, Tianyu
%A Wang, Hecong
%A Lu, Yang
%A Yang, Jiankun
%A Tang, Jiamu
%A Bai, Zhen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-systematic
%X Analogy is a quintessential human cognitive capability that supports knowledge transfer across domains. While researchers have long studied analogy generation, evaluation, and reasoning through symbolic and connectionist approaches, the rise of large language models (LLMs) has introduced new generation systems and evaluation methods. However, a systematic understanding of this emerging landscape and its connection to prior work remains underexplored. To address this gap, we conduct a PRISMA-based systematic literature review spanning four decades across six databases. We identify three key observations: first, generation and evaluation methods are strongly shaped by analogy granularity, such as lexical versus compositional analogies; second, human-in-the-loop design plays an important role in LLM-based analogy generation systems; and third, the field remains divided over whether LLMs perform genuine analogical reasoning. Based on our review, we propose practical guidelines for future work on analogy generation, including potential evaluation dimensions for lexical and compositional tasks.
%U https://aclanthology.org/2026.findings-emnlp.463/
%P 9710-9734
Markdown (Informal)
[A Systematic Review of Analogy Generation and Evaluation: Methods, Metrics, and Challenges](https://aclanthology.org/2026.findings-emnlp.463/) (Wu et al., Findings 2026)
ACL
- Shutong Wu, Tianyu Zhang, Hecong Wang, Yang Lu, Jiankun Yang, Jiamu Tang, and Zhen Bai. 2026. A Systematic Review of Analogy Generation and Evaluation: Methods, Metrics, and Challenges. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 9710–9734, Budapest, Hungary. Association for Computational Linguistics.