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.

翻译：我们介绍了ComplexTempQA，这是一个包含超过1亿个问答对的大规模数据集，旨在应对时序问答中的挑战。ComplexTempQA在规模和范围上显著超越了现有的基准数据集，如HOTPOTQA、TORQUE和TEQUILA。该数据集利用来自维基百科和维基数据的数据，涵盖了跨越二十多年的问题，并提供了无与伦比的主题广度。我们引入了一个独特的分类法，将问题归类为属性类、比较类和计数类问题，每一类都围绕事件、实体和时间段展开。ComplexTempQA的一个突出特点是其问题的高度复杂性，这些问题需要诸如跨时间比较、时间聚合以及涉及时序事件排序和实体识别的多跳推理等有效回答能力。此外，每个问题都附有详细的元数据，包括特定的时间范围，从而能够全面评估和增强大型语言模型的时序推理能力。ComplexTempQA既可作为开发复杂人工智能模型的测试平台，也可作为推进问答、信息检索和语言理解研究的基础。