ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering
<|reference_start|>ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering: We introduce ComplexTempQA, a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. ComplexTempQA significantly surpasses existing benchmarks like HOTPOTQA, TORQUE, and TEQUILA in scale and scope. Utilizing data from Wikipedia and Wikidata, the dataset covers questions spanning over two decades and offers an unmatched breadth of topics. We introduce a unique taxonomy that categorizes questions as attributes, comparisons, and counting questions, each revolving around events, entities, and time periods. One standout feature of ComplexTempQA is the high complexity of its questions, which demand effective 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 and enhancement of the temporal reasoning abilities of large language models. ComplexTempQA serves both as a testing ground for developing sophisticated AI models and as a foundation for advancing research in question answering, information retrieval, and language understanding.<|reference_end|>
@article{gruber2024complextempqa:,
    title={ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question
  Answering},
    author={Raphael Gruber, Abdelrahman Abdallah, Michael F"arber, Adam Jatowt},
    journal={arXiv preprint arXiv:2406.04866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04866},
    primaryClass={cs.CL}
}