It's High Time: A Survey of Temporal Question Answering

• URL: http://arxiv.org/abs/2505.20243v3
• Date: Mon, 04 Aug 2025 14:52:16 GMT
• Title: It's High Time: A Survey of Temporal Question Answering
• Authors: Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari, Avishek Anand, Adam Jatowt,
• Abstract summary: Temporal Question Answering (TQA) focuses on answering questions involving temporal constraints or context.<n>Recent advances in TQA enabled by neural models and Large Language Models (LLMs)<n> benchmark datasets and evaluation strategies designed to test temporal robustness, recency awareness, and generalization.
• Score: 17.07150094603319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a research area that focuses on answering questions involving temporal constraints or context. As the amount of time-stamped content from sources like news articles, web archives, and knowledge bases increases, systems must address challenges such as detecting temporal intent, normalizing time expressions, ordering events, and reasoning over evolving or ambiguous facts. We focus on recent advances in TQA enabled by neural architectures, especially transformer-based models and Large Language Models (LLMs), highlighting progress in temporal language modeling, retrieval-augmented generation (RAG), and temporal reasoning. We also discuss benchmark datasets and evaluation strategies designed to test temporal robustness, recency awareness, and generalization.

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