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

arXiv: https://arxiv.org/abs/2505.20243 · HTML: https://mlsanity.com/paper/2505.20243/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 173 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 30 of 75)

- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://mlsanity.com/paper/2102.01951/) (2021)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models](https://mlsanity.com/paper/2205.11388/) (2022)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models](https://mlsanity.com/paper/2410.23266/) (2024)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [TIMEDIAL: Temporal Commonsense Reasoning in Dialog](https://mlsanity.com/paper/2106.04571/) (2021)
- [FinTextQA: A Dataset for Long-form Financial Question Answering](https://mlsanity.com/paper/2405.09980/) (2024)
- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://mlsanity.com/paper/2005.00792/) (2020)
- [ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning](https://mlsanity.com/paper/2012.15283/) (2020)
- [ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections](https://mlsanity.com/paper/2109.03438/) (2021)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [Set the Clock: Temporal Alignment of Pretrained Language Models](https://mlsanity.com/paper/2402.16797/) (2024)

## Built on by (most cited first, 14 of 14)

- [Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies](https://mlsanity.com/paper/2508.10523/) (2025)
- [Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG](https://mlsanity.com/paper/2511.10523/) (2025)
- [TEMPO: A Realistic Multi-Domain Benchmark for Temporal Reasoning-Intensive Retrieval](https://mlsanity.com/paper/2601.09523/) (2026)
- [SwiftMem: Fast Agentic Memory via Query-aware Indexing](https://mlsanity.com/paper/2601.08160/) (2026)
- [Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval](https://mlsanity.com/paper/2601.05549/) (2026)
- [Evaluating Commercial AI Chatbots as News Intermediaries](https://mlsanity.com/paper/2605.22785/) (2026)
- [From Events to Trending: A Multi-Stage Hotspots Detection Method Based on Generative Query Indexing](https://mlsanity.com/paper/2601.05258/) (2025)
- [ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals](https://mlsanity.com/paper/2604.06997/) (2026)
- [DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval](https://mlsanity.com/paper/2605.31377/) (2026)
- [IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval](https://mlsanity.com/paper/2606.06044/) (2026)
- [Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law](https://mlsanity.com/paper/2608.09393/) (2026)
- [Contract Memory Compiler: Resolve, Then Traverse](https://mlsanity.com/paper/2609.32658/) (2026)
- [Who Warmed the Archives? LLMs Overestimate Historical Warmth](https://mlsanity.com/paper/2609.37499/) (2026)
- [ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models](https://mlsanity.com/paper/2610.20472/) (2026)

## Abstract (arXiv)

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 time-stamped content from sources like news articles, web archives, and knowledge bases continues to grow, TQA systems must address challenges such as detecting temporal intent, normalizing time expressions, ordering events, and reasoning over evolving or ambiguous facts. We organize existing work through a unified perspective that captures the interaction between corpus temporality, question temporality, and model capabilities, enabling a systematic comparison of datasets, tasks, and approaches. We review 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,