@inproceedings{li-etal-2025-past,
title = "Past Meets Present: Creating Historical Analogy with Large Language Models",
author = "Li, Nianqi and
Yuan, Siyu and
Chen, Jiangjie and
Liang, Jiaqing and
Wei, Feng and
Liang, Zujie and
Yang, Deqing and
Xiao, Yanghua",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.200/",
doi = "10.18653/v1/2025.acl-long.200",
pages = "3942--3957",
ISBN = "979-8-89176-251-0",
abstract = "Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method. Resources of this paper can be found at https://anonymous.4open.science/r/Historical-Analogy-of-LLMs-FC17"
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<abstract>Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method. Resources of this paper can be found at https://anonymous.4open.science/r/Historical-Analogy-of-LLMs-FC17</abstract>
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%0 Conference Proceedings
%T Past Meets Present: Creating Historical Analogy with Large Language Models
%A Li, Nianqi
%A Yuan, Siyu
%A Chen, Jiangjie
%A Liang, Jiaqing
%A Wei, Feng
%A Liang, Zujie
%A Yang, Deqing
%A Xiao, Yanghua
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-past
%X Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method. Resources of this paper can be found at https://anonymous.4open.science/r/Historical-Analogy-of-LLMs-FC17
%R 10.18653/v1/2025.acl-long.200
%U https://aclanthology.org/2025.acl-long.200/
%U https://doi.org/10.18653/v1/2025.acl-long.200
%P 3942-3957
Markdown (Informal)
[Past Meets Present: Creating Historical Analogy with Large Language Models](https://aclanthology.org/2025.acl-long.200/) (Li et al., ACL 2025)
ACL
- Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, and Yanghua Xiao. 2025. Past Meets Present: Creating Historical Analogy with Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3942–3957, Vienna, Austria. Association for Computational Linguistics.