# ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

arXiv: https://arxiv.org/abs/2406.04866 · HTML: https://mlsanity.com/paper/2406.04866/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [Meta-Learning Online Adaptation of Language Models](https://mlsanity.com/paper/2305.15076/) (2023)
- [Online Adaptation of Language Models with a Memory of Amortized Contexts](https://mlsanity.com/paper/2403.04317/) (2024)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models](https://mlsanity.com/paper/2109.01951/) (2021)
- [Quiz-Style Question Generation for News Stories](https://mlsanity.com/paper/2102.09094/) (2021)
- [BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information](https://mlsanity.com/paper/2204.13032/) (2022)
- [SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples](https://mlsanity.com/paper/2308.03671/) (2023)
- [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://mlsanity.com/paper/2308.00002/) (2023)
- [ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages](https://mlsanity.com/paper/2403.17859/) (2024)
- [Question Answering in Natural Language: the Special Case of Temporal Expressions](https://mlsanity.com/paper/2311.14087/) (2023)

## Built on by (most cited first, 6 of 6)

- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation](https://mlsanity.com/paper/2508.16998/) (2025)
- [RASTeR: Robust, Agentic, and Structured Temporal Reasoning](https://mlsanity.com/paper/2406.19538/) (2024)
- [HistoryBankQA: Multilingual Temporal Question Answering on Historical Events](https://mlsanity.com/paper/2509.12720/) (2025)
- [Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding](https://mlsanity.com/paper/2608.08512/) (2026)
- [Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models](https://mlsanity.com/paper/2508.02045/) (2025)

## Abstract (arXiv)

We introduce textscComplexTempQA,footnoteDataset and code available at: https://github.com/DataScienceUIBK/ComplexTempQA a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. textscComplexTempQA significantly surpasses existing benchmarks in scale and scope. Utilizing Wikipedia and Wikidata, the dataset covers questions spanning over two decades and offers an unmatched scale. We introduce a new taxonomy that categorizes questions as textitattributes, textitcomparisons, and textitcounting questions, revolving around events, entities, and time periods, respectively. A standout feature of textscComplexTempQA is the high complexity of its questions, which demand reasoning capabilities for answering such as across-time comparison, temporal aggregation, and multi-hop reasoning involving temporal event ordering and entity recognition. Additionally, each question is accompanied by detailed metadata, including specific time scopes, allowing for comprehensive evaluation of temporal reasoning abilities of large language models.