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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- question-answering |
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- table-question-answering |
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- text-generation |
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language: |
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- en |
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tags: |
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- croissant |
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pretty_name: UDA-QA |
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size_categories: |
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- 10K<n<100K |
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config_names: |
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- feta |
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- nq |
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- paper_text |
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- paper_tab |
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- fin |
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- tat |
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dataset_info: |
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- config_name: feta |
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features: |
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- name: doc_name |
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dtype: string |
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- name: q_uid |
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dtype: string |
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- name: question |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: doc_url |
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dtype: string |
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- config_name: nq |
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features: |
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- name: doc_name |
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dtype: string |
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- name: q_uid |
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dtype: string |
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- name: question |
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dtype: string |
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- name: short_answer |
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dtype: string |
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- name: long_answer |
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dtype: string |
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- name: doc_url |
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dtype: string |
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- config_name: paper_text |
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features: |
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- name: doc_name |
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dtype: string |
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- name: q_uid |
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dtype: string |
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- name: question |
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dtype: string |
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- name: answer_1 |
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dtype: string |
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- name: answer_2 |
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dtype: string |
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- name: answer_3 |
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dtype: string |
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- config_name: paper_tab |
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features: |
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- name: doc_name |
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dtype: string |
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- name: q_uid |
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dtype: string |
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- name: question |
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dtype: string |
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- name: answer_1 |
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dtype: string |
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- name: answer_2 |
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dtype: string |
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- name: answer_3 |
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dtype: string |
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- config_name: fin |
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features: |
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- name: doc_name |
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dtype: string |
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- name: q_uid |
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dtype: string |
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- name: question |
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dtype: string |
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- name: answer_1 |
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dtype: string |
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- name: answer_2 |
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dtype: string |
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configs: |
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- config_name: feta |
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data_files: |
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- split: test |
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path: feta/test* |
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- config_name: nq |
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data_files: |
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- split: test |
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path: nq/test* |
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- config_name: paper_text |
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data_files: |
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- split: test |
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path: paper_text/test* |
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- config_name: paper_tab |
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data_files: |
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- split: test |
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path: paper_tab/test* |
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- config_name: fin |
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data_files: |
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- split: test |
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path: fin/test* |
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- config_name: tat |
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data_files: |
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- split: test |
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path: tat/test* |
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--- |
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# Dataset Card for Dataset Name |
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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. |
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It includes six sub-datasets across three pivotal domains: finance, academia, and knowledge bases. |
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Each data item within UDA is structured as a triplet: document-question-answer pair. |
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The documents are retained in their original file formats without parsing or segmentation, to mirror the authenticity of real-world applications. |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Curated by:** Yulong Hui, Tsinghua University |
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- **Language(s) (NLP):** English |
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- **License:** CC-BY-SA-4.0 |
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- **Repository:** https://github.com/qinchuanhui/UDA-Benchmark |
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## Uses |
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### Direct Use |
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Question-answering tasks on complete unstructured documents. |
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After loading the dataset, you should also **download the sourc document files from the folder `src_doc_files`**. |
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### Extended Use |
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Evaluate the effectiveness of retrieval strategies using the evidence provided in the `extended_qa_info` folder. |
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Directly assess the performance of LLMs in numerical reasoning and table reasoning, using the evidence in the `extended_qa_info` folder as context. |
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Assess the effectiveness of parsing strategies on unstructured PDF documents. |
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## Dataset Structure |
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<!-- 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. --> |
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Field Name | Field Value | Description| Example |
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--- | --- | ---|--- |
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doc_name | string | name of the source document | 1912.01214 |
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q_uid | string | unique id of the question | 9a05a5f4351db75da371f7ac12eb0b03607c4b87 |
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question | string | raised question | which datasets did they experiment with? |
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answer <br />or answer_1, answer_2 <br />or short_answer, long_answer | string | ground truth answer/answers | Europarl, MultiUN |
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**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. |
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## Dataset Creation |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### Data Collection and Processing |
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We collect the Q&A labels from the open-released datasets (i.e., source datasets), which are all annotated by human participants. |
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Then we conduct a series of essential constructing actions, including source-document identification, categorization, filtering, data transformation. |
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#### Who are the source data producers? |
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[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). |
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[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. |
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[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). |
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[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. |
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[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. |
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## Considerations for Using the Data |
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#### Personal and Sensitive Information |
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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. |
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<!-- ## Citation [optional] --> |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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<!-- **BibTeX:** --> |
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## Dataset Card Contact |
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[email protected] |
