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        <title>Consistent Discourse-level Temporal Relation Extraction Using Large Language Models</title>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
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            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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    <abstract>Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs’ temporal relation extraction capabilities: context selection, prompts inspired by Allen’s interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.</abstract>
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%0 Conference Proceedings
%T Consistent Discourse-level Temporal Relation Extraction Using Large Language Models
%A Fan, Yi
%A Strube, Michael
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F fan-strube-2025-consistent
%X Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs’ temporal relation extraction capabilities: context selection, prompts inspired by Allen’s interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.
%U https://aclanthology.org/2025.findings-emnlp.1010/
%P 18605-18622

Markdown (Informal)

[Consistent Discourse-level Temporal Relation Extraction Using Large Language Models](https://aclanthology.org/2025.findings-emnlp.1010/) (Fan & Strube, Findings 2025)

• Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (Fan & Strube, Findings 2025)

ACL

• Yi Fan and Michael Strube. 2025. Consistent Discourse-level Temporal Relation Extraction Using Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 18605–18622, Suzhou, China. Association for Computational Linguistics.