Enhancing Temporal Understanding in LLMs for Semi-structured Tables (2025.findings-naacl)
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| Challenge: | Temporal reasoning over tabular data presents significant challenges for large language models (LLMs), as evidenced by recent research. |
| Approach: | They propose a method that enhances LLMs' temporal reasoning over tabular data by using standard prompts and introduce a novel approach, C.L.E.A.R. |
| Outcome: | The proposed method improves evidence-based reasoning across models and indirect supervision with auxiliary unstructured data significantly boosts model performance in these tasks. |
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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. |
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| Challenge: | Semi-structured data often include temporal information about entities, either implicitly or explicitly. |
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| Challenge: | Existing methods for temporal tabular question answering are inconsistent and fail to provide the variability needed to thoroughly evaluate models. |
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| Challenge: | despite advances in natural language processing, temporal reasoning is still a challenge . despite advancements in NLP, current language models have yet to reach human level in this domain . |
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
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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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| Challenge: | Large Language Models (LLMs) exhibit notable deficiencies in temporal reasoning . phrasing changes can lead LLMs to produce inconsistent outputs . |
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Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
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Large Language Models Can Learn Temporal Reasoning (2024.acl-long)
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| Challenge: | Temporal reasoning (TR) is a fundamental ability of large language models (LLMs) however, there is neo-standard methods to perform TR, which are not suitable for large language model applications. |
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Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models (2023.acl-long)
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| Challenge: | Recent time-dependent question answering datasets tend to be biased in either their coverage of time spans or question types. |
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