mlsanityData to 7 Oct 2026

Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

arXiv 2409.16909 ↗First posted 25 Sep 2024cs.CL cs.AIcited by 9

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 27 of its 33 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

27 of 33 references.

References · 27

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15 more
  1. 2017SearchQA: A New Q&A Dataset Augmented with Context from a Search Enginecited by 221
  2. 2021A Dataset for Answering Time-Sensitive Questionscited by 150
  3. 2021SituatedQA: Incorporating Extra-Linguistic Contexts into QAcited by 145
  4. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Frameworkcited by 139
  5. 2022StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Modelscited by 123
  6. 2020Entities as Experts: Sparse Memory Access with Entity Supervisioncited by 100
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  13. 2021Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Eventscited by 22
  14. 2023Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answeringcited by 6
  15. 2024Continual Learning for Temporal-Sensitive Question Answeringcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
  2. 2023Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models
  3. 2023Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic
  4. 2026TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
  5. 2023Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning
  6. 2023Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering
  7. 2025Plan of Knowledge: Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering
  8. 2024Timo: Towards Better Temporal Reasoning for Language Models