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  ## Overview
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- **watsonxDocsQA** is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
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  - **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation ([main page - crawl March 2024](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
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- - **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers.The QA pairs are crafted as follows:
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  - **25 questions**: Human-generated by two subject matter experts.
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  - **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [Yehudai et al. 2024](https://arxiv.org/pdf/2401.14367).
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  ## Overview
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+ **watsonxDocsQA** is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and is designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
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  - **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation ([main page - crawl March 2024](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
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+ - **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers. The QA pairs are crafted as follows:
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  - **25 questions**: Human-generated by two subject matter experts.
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  - **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [Yehudai et al. 2024](https://arxiv.org/pdf/2401.14367).
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