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---
license: cc0-1.0
task_categories:
- question-answering
language:
- en
size_categories:
- 100M<n<1B
---
# ComplexTempQA Dataset
ComplexTempQA is a large-scale dataset designed for complex temporal question answering (TQA). It consists of over 100 million question-answer pairs, making it one of the most extensive datasets available for TQA. The dataset is generated using data from Wikipedia and Wikidata and spans questions over a period of 36 years (1987-2023).
## Dataset Description
ComplexTempQA categorizes questions into three main types:
- Attribute Questions
- Comparison Questions
- Counting Questions
These categories are further divided based on their relation to events, entities, or time periods.
### Question Types and Counts
| Question Type | Subtype | Count |
|-----------------------|---------------------|---------------|
| Attribute | Event | 83,798 |
| Attribute | Entity | 84,079 |
| Attribute | Time | 9,454 |
| Comparison | Event | 25,353,340 |
| Comparison | Entity | 74,678,117 |
| Comparison | Time | 54,022,952 |
| Counting | Event | 18,325 |
| Counting | Entity | 10,798 |
| Counting | Time | 12,732 |
| Multi-Hop | | 76,933 |
| Unnamed Event | | 8,707,123 |
| **Total** | | **100,228,457**|
### Metadata
Each question in the dataset is accompanied by detailed metadata, including:
- Type of question based on taxonomy
- Wikidata IDs of the questioned entities or events
- Country information for both questions and answers
- Difficulty rating (easy or hard)
- Time span related to the question
## Dataset Characteristics
### Size
ComplexTempQA comprises over 100 million question-answer pairs, focusing on events, entities, and time periods from 1987 to 2023.
### Complexity
Questions require advanced reasoning skills, including multi-hop question answering, temporal aggregation, and across-time comparisons.
### Taxonomy
The dataset follows a unique taxonomy categorizing questions into attributes, comparisons, and counting types, ensuring comprehensive coverage of temporal queries.
### Evaluation
The dataset has been evaluated for readability, ease of answering before and after web searches, and overall clarity. Human raters have assessed a sample of questions to ensure high quality.
## Usage
### Evaluation and Training
ComplexTempQA can be used for:
- Evaluating the temporal reasoning capabilities of large language models (LLMs)
- Fine-tuning language models for better temporal understanding
- Developing and testing retrieval-augmented generation (RAG) systems
### Research Applications
The dataset supports research in:
- Temporal question answering
- Information retrieval
- Language understanding
### Adaptation and Continual Learning
ComplexTempQA's temporal metadata facilitates the development of online adaptation and continual training approaches for LLMs, aiding in the exploration of time-based learning and evaluation.
## Access
The dataset and code are freely available at [https://github.com/DataScienceUIBK/ComplexTempQA](https://github.com/DataScienceUIBK/ComplexTempQA). |