| Challenge: | Existing studies on pre-trained language models for dialog reasoning fail to understand context correctly. |
| Approach: | They propose to use a crowd-sourced English task and a time-based task to test models' temporal reasoning abilities in dialogs. |
| Outcome: | The proposed task and crowd-sourced English challenge set show that even the best performing models struggle on this task compared to humans. |
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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). |
| Approach: | They propose to use 3 prompting strategies to evaluate 8 different LLMs across 6 datasets and 2 Code Generation LMs to perform the analysis. |
| Outcome: | The proposed models perform better on NLP tasks than the standard models on the same dataset. |
TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning (2026.findings-acl)
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| Challenge: | Reasoning-oriented language models expose explicit reasoning as a long, front-loaded chain of “thinking” tokens before the main output, either always enabled or externally toggled at inference time. |
| Approach: | They introduce a behavioral alignment framework that learns explicit reasoning as a context-triggered control policy rather than a fixed response mode. |
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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. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
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Toward Building a Language Model for Understanding Temporal Commonsense (2022.aacl-srw)
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| Challenge: | Pre-trained language models such as BERT are still poor in temporal reasoning . commonsense reasoning is crucial for natural language processing (NLP) |
| Approach: | They propose to use multi-step fine-tuning and masked language modeling to predict mangled temporal indicators that are crucial for commonsense reasoning. |
| Outcome: | The proposed model improves performance on multiple time-related tasks. |
Human Temporal Inferences Go Beyond Aspectual Class (2024.eacl-long)
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| Challenge: | Existing work on aspectual classification in English has been motivated as a pre-requisite for Natural Language Understanding (NLU) in cases where temporal reasoning is required. |
| Approach: | They propose to classify English verb phrases into situation aspect categories by gathering crowd-sourced judgements from non-expert, native English participants. |
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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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Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in many reasoning tasks, but temporal reasoning remains challenging due to its intrinsic complexity. |
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TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)
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| Challenge: | Grasping the concept of time is a fundamental facet of human cognition. |
| Approach: | They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena. |
| Outcome: | The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning . |
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. |
| Approach: | They propose a temporal reasoning framework based on temporal span extraction and time-sensitive reinforcement learning to improve the temporal ability of large language models. |
| Outcome: | The proposed framework improves the temporal reasoning capability of large language models by using temporal span extraction and time-sensitive reinforcement learning. |
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing (2020.emnlp-main)
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| Challenge: | Recent work shows that deep contextualized language models (LMs) can extract temporal relations between events and time expressions. |
| Approach: | They propose a temporal relation extraction technique which extracts temporal relations between events and time expressions. |
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