|
--- |
|
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} |
|
} |
|
``` |
|
|
