Update README.md

README.md

@@ -6,4 +6,63 @@ language:
 - en
 size_categories:
 - 1K<n<10K
----
+---
+
+
+## Dataset Summary
+
+**RetrievalQA** is a short-form open-domain question answering (QA) dataset consisting of 1,271 questions covering new world and long-tail knowledge. 
+We ensure the knowledge necessary to answer the questions is absent from LLMs. Therefore, LLMs must truthfully decide whether to retrieve to be able to answer the questions correctly. 
+
+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 ..."
+    }, ...
+  ]
+}
+```
+
+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.
+
+## 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}
+}
+```
+