Dataset Summary

RetrievalQA is a short-form open-domain question answering (QA) dataset comprising 2,785 questions covering new world and long-tail knowledge. It contains 1,271 questions needing external knowledge retrieval and 1,514 questions that most LLMs can answer with internal parametric knowledge.

RetrievalQA enables us to evaluate the effectiveness of adaptive retrieval-augmented generation (RAG) approaches, an aspect predominantly overlooked in prior studies and recent RAG evaluation systems, which focus only on task performance, the relevance of retrieval context or the faithfulness of answers.

Dataset Sources

Repository: https://github.com/hyintell/RetrievalQA
Paper: https://arxiv.org/abs/2402.16457

Dataset Structure

Here is an example of a data instance:

{
  "data_source": "realtimeqa",
  "question_id": "realtimeqa_20231013_1",
  "question": "What percentage of couples are 'sleep divorced', according to new research?",
  "ground_truth": ["15%"],
  "context": [
    {
      "title": "Do We Sleep Longer When We Share a Bed?",
      "text": "1.4% of respondents have started a sleep divorce, or sleeping separately from their partner, and maintained it in the past year. Adults who have ..."
    }, ...
  ],
  "param_knowledge_answerable": 0
}

where:

data_source: the origin dataset of the question comes from
question: the question
ground_truth: a list of possible answers
context: a list of dictionaries of retrieved relevant evidence. Note that the title of the document might be empty.
param_knowledge_answerable: 0 indicates the question needs external retrieval; 1 indicates the question can be answerable using its parametric knowledge

Citation

@misc{zhang2024retrievalqa,
      title={RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering},
      author={Zihan Zhang and Meng Fang and Ling Chen},
      year={2024},
      eprint={2402.16457},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}