Update README.md

README.md

@@ -58,21 +58,11 @@ dataset_info:
 
 ## Overview
 
-**watsonxDocsQA** is a new open-source dataset and benchmark derived from enterprise product documentation. Designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation, it consists of two key components:
-
-- **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation (link to the documentation source : [https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html](#)).
-- **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers.
-
-The QA pairs are crafted as follows:
-- **25 questions**: Human-generated by two subject matter experts.
-- **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [this paper](https://arxiv.org/pdf/2401.14367).
-
-### Key Details
-- **Number of Samples**: 
-  - Corpus: 1,144 documents
-  - QA Set: 75 questions
-- **Format**: CSV
-- **License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+**watsonxDocsQA**  is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
+- **Documents**: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation ([main page](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
+- **Benchmark**: A set of 75 question-answer (QA) pairs with gold document labels and answers.The QA pairs are crafted as follows:
+  - **25 questions**: Human-generated by two subject matter experts.
+  - **50 questions**: Synthetically generated using the `tiiuae/falcon-180b` model, then manually filtered and reviewed for quality. The methodology is detailed in [Yehudai et al. 2024](https://arxiv.org/pdf/2401.14367).
 
 ---
 
@@ -104,7 +94,6 @@ The QA dataset includes these fields:
 
 ## Samples
 
----
 
 Below is an example from the `question_answers` dataset: