Update README.md

README.md

@@ -1,3 +1,69 @@
----
-license: mit
----
+---
+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}
+}
+```