DataScienceUIBK
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README.md
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#
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## Dataset Description
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- Attribute Questions
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- Comparison Questions
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- Counting Questions
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## Dataset Characteristics
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### Size
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### Complexity
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Questions require advanced reasoning skills, including multi-hop question answering, temporal aggregation, and across-time comparisons.
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## Usage
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### Evaluation and Training
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- Evaluating the temporal reasoning capabilities of large language models (LLMs)
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- Fine-tuning language models for better temporal understanding
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- Developing and testing retrieval-augmented generation (RAG) systems
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- Language understanding
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### Adaptation and Continual Learning
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## Access
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size_categories:
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# ComplexTempQA Dataset
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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).
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## Dataset Description
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ComplexTempQA categorizes questions into three main types:
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- Attribute Questions
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- Comparison Questions
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- Counting Questions
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## Dataset Characteristics
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### Size
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ComplexTempQA comprises over 100 million question-answer pairs, focusing on events, entities, and time periods from 1987 to 2023.
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### Complexity
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Questions require advanced reasoning skills, including multi-hop question answering, temporal aggregation, and across-time comparisons.
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## Usage
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### Evaluation and Training
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ComplexTempQA can be used for:
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- Evaluating the temporal reasoning capabilities of large language models (LLMs)
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- Fine-tuning language models for better temporal understanding
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- Developing and testing retrieval-augmented generation (RAG) systems
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- Language understanding
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### Adaptation and Continual Learning
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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.
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## Access
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