---
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
# - config_name: tat
#   features:
#   - name: doc_name
#     dtype: string
#   - name: q_uid
#     dtype: string
#   - name: question
#     dtype: string
#   - name: answer
#   - name: answer_scale
#     dtype: string
#   - name: answer_type
#     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

<!-- 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. -->
#### 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  <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 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

<!-- 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