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Datasets:
qinchuanhui
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UDA-QA

Tasks:
Question Answering
Table Question Answering
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
croissant
DOI:
Libraries:
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pandas
License:
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metadata
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 (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

Dataset Description

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.

More usage guidelines please refer to https://github.com/qinchuanhui/UDA-Benchmark

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

Descriptive Statistics

Sub Dataset (folder_name) Source Domain Doc Format Doc Num Q&A Num Avg #Words Avg #Pages Q&A Types
FinHybrid (fin) finance reports PDF 788 8190 76.6k 147.8 arithmetic
TatHybrid (tat) finance reports PDF 170 14703 77.5k 148.5 extractive, counting, arithmetic
PaperTab (paper_tab) academic papers PDF 307 393 6.1k 11.0 extractive, yes/no, free-form
PaperText (paper_text) academic papers PDF 1087 2804 5.9k 10.6 extractive, yes/no, free-form
FetaTab (feta) wikipedia PDF & HTML 878 1023 6.0k 14.9 free-form
NqText (nq) wikipedia PDF & HTML 645 2477 6.1k 14.9 extractive

Data Fields

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 FinHybrid, PaperTab and PaperText) or short_answer, long_answer (in NqText); In sub-dataset TatHybrid, the answer is organized as a sequence, due to the involvement of the multi-span Q&A type. Additionally, some sub-datasets may have unique data fields. For example, doc_url in FetaTab and NqText describes the Wikipedia URL page, while answer_type and answer_scale in TatHybrid provide extended answer references.

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 sources of data are publicly available reports, papers and wikipedia pages, which have been commonly utilized and accepted by the broader community.

Dataset Card Contact

qinchuanhui@gmail.com