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        <title>Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models</title>
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    <name type="personal">
        <namePart type="given">Jongho</namePart>
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    <name type="personal">
        <namePart type="given">Seung-won</namePart>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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            <namePart type="given">Joyce</namePart>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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            <namePart type="given">Mohammad</namePart>
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    <abstract>Despite the advanced capabilities of large language models (LLMs), their temporal reasoning ability remains underdeveloped. Prior works have highlighted this limitation, particularly in maintaining temporal consistency when understanding event relations. For example, models often confuse mutually exclusive temporal relations like “before” and “after” between events and make inconsistent predictions. In this work, we tackle the issue of temporal inconsistency in LLMs by proposing a novel counterfactual prompting approach. Our method generates counterfactual questions and enforces collective constraints, enhancing the model’s consistency. We evaluate our method on multiple datasets, demonstrating significant improvements in event ordering for explicit and implicit events and temporal commonsense understanding, by effectively addressing temporal inconsistencies.</abstract>
    <identifier type="citekey">kim-hwang-2025-counterfactual</identifier>
    <identifier type="doi">10.18653/v1/2025.acl-short.97</identifier>
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%0 Conference Proceedings
%T Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models
%A Kim, Jongho
%A Hwang, Seung-won
%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 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F kim-hwang-2025-counterfactual
%X Despite the advanced capabilities of large language models (LLMs), their temporal reasoning ability remains underdeveloped. Prior works have highlighted this limitation, particularly in maintaining temporal consistency when understanding event relations. For example, models often confuse mutually exclusive temporal relations like “before” and “after” between events and make inconsistent predictions. In this work, we tackle the issue of temporal inconsistency in LLMs by proposing a novel counterfactual prompting approach. Our method generates counterfactual questions and enforces collective constraints, enhancing the model’s consistency. We evaluate our method on multiple datasets, demonstrating significant improvements in event ordering for explicit and implicit events and temporal commonsense understanding, by effectively addressing temporal inconsistencies.
%R 10.18653/v1/2025.acl-short.97
%U https://aclanthology.org/2025.acl-short.97/
%U https://doi.org/10.18653/v1/2025.acl-short.97
%P 1210-1225

Markdown (Informal)

[Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models](https://aclanthology.org/2025.acl-short.97/) (Kim & Hwang, ACL 2025)

• Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models (Kim & Hwang, ACL 2025)

ACL

• Jongho Kim and Seung-won Hwang. 2025. Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1210–1225, Vienna, Austria. Association for Computational Linguistics.