Update README.md

README.md

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 # Dataset Card for Dataset Name
 
-UDA is a benchmark suite for Retrieval Augmented Generation (RAG) in real-world document analysis, which involves 2965 documents and 29590 expert-annotated Q&A pairs.
-It includes six sub-datasets across three pivotal domains: finance, academia, and knowledge bases. 
-Each data item within UDA is structured as a triplet: document-question-answer pair. 
-The documents are retained in their original file formats without parsing or segmentation, to mirror the authenticity of real-world applications.
+UDA (Unstructured Document Analysis) is a benchmark suite for Retrieval Augmented Generation (RAG) in real-world document analysis. 
+Each entry in the UDA dataset is organized as a *document-question-answer* triplet, where a question is raised from the document, accompanied by a corresponding ground-truth answer. 
+The documents are retained in their original file formats without parsing or segmentation; 
+they consist of both textual and tabular data, reflecting the complex nature of real-world analytical scenarios.
 
 ## Dataset Details
 
@@ -150,6 +150,8 @@ Question-answering tasks on complete unstructured documents.
 
 After loading the dataset, you should also **download the sourc document files from the folder `src_doc_files`**.
 
+More usage guidelines please refer to https://github.com/qinchuanhui/UDA-Benchmark 
+
 
 ### Extended Use
 
@@ -200,21 +202,22 @@ Then we conduct a series of essential constructing actions, including source-doc
 
 #### Who are the source data producers?
 
-[1]CHEN, Z., CHEN, W., SMILEY, C., SHAH, S., BOROVA, I., LANGDON, D., MOUSSA, R., BEANE, M., HUANG, T.-H., ROUTLEDGE, B., ET AL. Finqa: A dataset of numerical reasoning over financial data. arXiv preprint arXiv:2109.00122 (2021).
+[1] CHEN, Z., CHEN, W., SMILEY, C., SHAH, S., BOROVA, I., LANGDON, D., MOUSSA, R., BEANE, M., HUANG, T.-H., ROUTLEDGE, B., ET AL. Finqa: A dataset of numerical reasoning over financial data. arXiv preprint arXiv:2109.00122 (2021).
 
-[2]ZHU, F., LEI, W., FENG, F., WANG, C., ZHANG, H., AND CHUA, T.-S. Towards complex document understanding by discrete reasoning. In Proceedings of the 30th ACM International Conference on Multimedia (2022), pp. 4857–4866.
+[2] ZHU, F., LEI, W., FENG, F., WANG, C., ZHANG, H., AND CHUA, T.-S. Towards complex document understanding by discrete reasoning. In Proceedings of the 30th ACM International Conference on Multimedia (2022), pp. 4857–4866.
 
-[3]DASIGI, P., LO, K., BELTAGY, I., COHAN, A., SMITH, N. A., AND GARDNER, M. A dataset of information-seeking questions and answers anchored in research papers. arXiv preprint arXiv:2105.03011 (2021).
+[3] DASIGI, P., LO, K., BELTAGY, I., COHAN, A., SMITH, N. A., AND GARDNER, M. A dataset of information-seeking questions and answers anchored in research papers. arXiv preprint arXiv:2105.03011 (2021).
 
-[4]NAN, L., HSIEH, C., MAO, Z., LIN, X. V., VERMA, N., ZHANG, R., KRYS ́ CIN ́ SKI, W., SCHOELKOPF, H., KONG, R., TANG, X., ET AL. Fetaqa: Free-form table question answering. Transactions of the Association for Computational Linguistics 10 (2022), 35–49.
+[4] NAN, L., HSIEH, C., MAO, Z., LIN, X. V., VERMA, N., ZHANG, R., KRYS ́ CIN ́ SKI, W., SCHOELKOPF, H., KONG, R., TANG, X., ET AL. Fetaqa: Free-form table question answering. Transactions of the Association for Computational Linguistics 10 (2022), 35–49.
 
-[5]KWIATKOWSKI, T., PALOMAKI, J., REDFIELD, O., COLLINS, M., PARIKH, A., ALBERTI, C., EPSTEIN, D., POLOSUKHIN, I., DEVLIN, J., LEE, K., ET AL. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics 7 (2019), 453–466.
+[5] KWIATKOWSKI, T., PALOMAKI, J., REDFIELD, O., COLLINS, M., PARIKH, A., ALBERTI, C., EPSTEIN, D., POLOSUKHIN, I., DEVLIN, J., LEE, K., ET AL. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics 7 (2019), 453–466.
 
 
 ## Considerations for Using the Data
 #### Personal and Sensitive Information
 
-The dataset doesn't contain data that might be considered personal, sensitive, or private. The source of data are public available reports, papers and wikipedia pages.
+The dataset doesn't contain data that might be considered personal, sensitive, or private. The sources of data are publicly available reports, papers and wikipedia pages, which have been commonly utilized and accepted by the broader community.
+
 
 
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