Update README.md

README.md

@@ -124,4 +124,96 @@ configs:
   data_files:
   - split: test
     path: tat/test*
----
+---
+# 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.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+
+
+- **Curated by:** Yulong Hui, Tsinghua University
+- **Language(s) (NLP):** English
+- **License:** CC-BY-SA-4.0
+- **Repository:** https://github.com/qinchuanhui/UDA-Benchmark
+
+## Uses
+
+### Direct Use
+
+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`**.
+
+
+### Extended Use
+
+Evaluate the effectiveness of retrieval strategies using the evidence provided in the `extended_qa_info` folder.
+
+Directly assess the performance of LLMs in numerical reasoning and table reasoning, using the evidence in the `extended_qa_info` folder as context.
+
+Assess the effectiveness of parsing strategies on unstructured PDF documents.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+Field Name | Field Value | Description| Example 
+--- | --- | ---|--- 
+doc_name | string | name of the source document | 1912.01214 
+q_uid | string      | unique id of the question | 9a05a5f4351db75da371f7ac12eb0b03607c4b87 
+question | string | raised question | which datasets did they experiment with? 
+answer  <br />or  answer_1, answer_2 <br />or short_answer,  long_answer | string | ground truth answer/answers | Europarl, MultiUN 
+
+**Additional Notes:** Some sub-datasets may have multiple ground_truth answers, where the answers are organized as `answer_1`, `answer_2` (in fin,  paper_tab and paper_text) or `short_answer`, `long_answer` (in nq); In sub-dataset tat, the answer is organized as a sequence, due to the  involvement of the multi-span Q&A type.
+
+
+## Dataset Creation
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### Data Collection and Processing
+
+We collect the Q&A labels from the open-released datasets (i.e., source  datasets), which are all annotated by human participants. 
+Then we conduct a series of essential constructing actions, including source-document identification, categorization, filtering, data transformation.
+
+#### 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).
+
+[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).
+
+[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.
+
+
+## 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.
+
+
+<!-- ## Citation [optional] -->
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+<!-- **BibTeX:** -->
+
+
+## Dataset Card Contact
+
+qinchuanhui@gmail.com