license: cc-by-sa-4.0
task_categories:
- question-answering
- table-question-answering
- text-generation
language:
- en
tags:
- croissant
pretty_name: UDA-QA
size_categories:
- 10K<n<100K
config_names:
- feta
- nq
- paper_text
- paper_tab
- fin
- tat
dataset_info:
- config_name: feta
features:
- name: doc_name
dtype: string
- name: q_uid
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: doc_url
dtype: string
- config_name: nq
features:
- name: doc_name
dtype: string
- name: q_uid
dtype: string
- name: question
dtype: string
- name: short_answer
dtype: string
- name: long_answer
dtype: string
- name: doc_url
dtype: string
- config_name: paper_text
features:
- name: doc_name
dtype: string
- name: q_uid
dtype: string
- name: question
dtype: string
- name: answer_1
dtype: string
- name: answer_2
dtype: string
- name: answer_3
dtype: string
- config_name: paper_tab
features:
- name: doc_name
dtype: string
- name: q_uid
dtype: string
- name: question
dtype: string
- name: answer_1
dtype: string
- name: answer_2
dtype: string
- name: answer_3
dtype: string
- config_name: fin
features:
- name: doc_name
dtype: string
- name: q_uid
dtype: string
- name: question
dtype: string
- name: answer_1
dtype: string
- name: answer_2
dtype: string
configs:
- config_name: feta
data_files:
- split: test
path: feta/test*
- config_name: nq
data_files:
- split: test
path: nq/test*
- config_name: paper_text
data_files:
- split: test
path: paper_text/test*
- config_name: paper_tab
data_files:
- split: test
path: paper_tab/test*
- config_name: fin
data_files:
- split: test
path: fin/test*
- config_name: tat
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
- 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
| 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 or answer_1, answer_2 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
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.