Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown promising performance in temporal reasoning tasks such as temporal question answering. |
| Approach: | They propose to use large language models to detect temporal relations between events with in-context learning and lightweight fine-tuning approaches to assess their performance. |
| Outcome: | The proposed models significantly underperform smaller encoder-only models based on RoBERTa in the Temporal Relation Classification task. |
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| Challenge: | Recent large language models lack a consistent temporal model of textual narratives . sentence ordering in unlabelled texts is only weakly correlated with event ordering . |
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Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored. |
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Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
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Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025.naacl-long)
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
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Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References? (2025.emnlp-main)
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| Challenge: | Existing efforts to ensure temporal consistency in large language models are lacking in time-sensitive fields . temporal reasoning is essential for time- sensitive fields such as finance and healthcare . a new benchmark aims to improve temporal referent consistency of LLMs . |
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Language Models Lack Temporal Generalization and Bigger is Not Better (2025.findings-acl)
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| Challenge: | 450 encoder models are fine-tuned on 15 data splits on a task to detect events in Early Modern Dutch archival texts. |
| Approach: | They propose to fine tune six encoder models that have been pretrained with very different data on a task in Early Modern Dutch archival texts. |
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ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) still face significant challenges in reasoning and arithmetic. |
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Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
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Can Language Models Serve as Temporal Knowledge Bases? (2022.findings-emnlp)
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| Challenge: | Existing studies have only considered language models as knowledge bases in a static setting . memorizing conflicting information is still challenging for LMs and hinders memorization of other unrelated one-to-one relationships. |
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TRANSIENTTABLES: Evaluating LLMs’ Reasoning on Temporally Evolving Semi-structured Tables (2025.naacl-long)
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| Challenge: | a recent study shows that large language models are limited in their ability to reason over time due to static datasets. |
| Approach: | They present a dataset that includes 3,971 questions derived from over 14,000 tables . they introduce a template-based question-generation pipeline that harnesses LLMs to refine questions . |
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