---
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
viewer: false
---


## 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:
```json
{
  "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

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

```bibtex
@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}
}
```