ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

We introduce $\text{textsc}ComplexTempQA$,\footnote{Dataset 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. $\text{textsc}ComplexTempQA$ 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 $\mathit{\text{attributes}}$, $\mathit{\text{comparisons}}$, and $\mathit{\text{counting}}$ questions, revolving around events, entities, and time periods, respectively. A standout feature of $\text{textsc}ComplexTempQA$ 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.