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watsonxDocsQA Dataset

Overview

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:

• Documents: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation (main page - crawl March 2024).
• Benchmark: A set of 75 question-answer (QA) pairs with gold document labels and answers. The QA pairs are crafted as follows:
  • 25 questions: Human-generated by two subject matter experts.
  • 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.

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Data Description

Corpus Dataset

The corpus dataset contains the following fields:

Field	Description
doc_id	Unique identifier for the document
title	Document title as it appears on the HTML page
document	Textual representation of the content
md_document	Markdown representation of the content
url	Origin URL of the document

Question-Answers Dataset

The QA dataset includes these fields:

Field	Description
question_id	Unique identifier for the question
question	Text of the question
correct_answer	Ground-truth answer
ground_truths_contexts_ids	List of ground-truth document IDs
ground_truths_contexts	List of grounding texts on which the answer is based

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Samples

Below is an example from the question_answers dataset:

• question_id: watsonx_q_2
• question: What foundation models have been built by IBM?
• correct_answer:
  "Foundation models built by IBM include:
  • granite-13b-chat-v2
  • granite-13b-chat-v1
  • granite-13b-instruct-v1"
• ground_truths_contexts_ids: B2593108FA446C4B4B0EF5ADC2CD5D9585B0B63C
• ground_truths_contexts: Foundation models built by IBM \n\nIn IBM watsonx.ai, ...

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Citation

If you decide to use this dataset, please consider citing our preprint

@misc{orbach2025analysishyperparameteroptimizationmethods,
      title={An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation}, 
      author={Matan Orbach and Ohad Eytan and Benjamin Sznajder and Ariel Gera and Odellia Boni and Yoav Kantor and Gal Bloch and Omri Levy and Hadas Abraham and Nitzan Barzilay and Eyal Shnarch and Michael E. Factor and Shila Ofek-Koifman and Paula Ta-Shma and Assaf Toledo},
      year={2025},
      eprint={2505.03452},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.03452}, 
}

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Contact

For questions or feedback, please:

• Email: benjams@il.ibm.com
• Or, open an pull request/discussion in this repository.

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