ibm-research/FeTaQARetrieval

FeTaQA Retrieval

Table retrieval benchmark dataset. Includes queries and relevance judgments across test split(s), with corpus in 2 format(s): corpus_md, corpus_structure.

Configs

Config	Description	Split(s)
default	Relevance judgments (qrels): qid, did, score	test
queries	Query IDs and text	test_queries
corpus_md	Markdown table representation	corpus_md
corpus_structure	Structured corpus with headers, cells, meta_data. text field corresponds to linearized Text + Table.	corpus_structure

corpus_structure additional fields

Field	Type	Description
meta_data	string	Table metadata / caption
headers	list[string]	Column headers
cells	list[string]	Flattened cell values

TableIR Benchmark Statistics

Dataset	Structured	#Train	#Dev	#Test	#Corpus
OpenWikiTables	✓	53.8k	6.6k	6.6k	24.7k
NQTables	✓	9.6k	1.1k	1k	170k
FeTaQA	✓	7.3k	1k	2k	10.3k
OTT-QA (small)	✓	41.5k	2.2k	--	8.8k
MultiHierTT	✗	--	929	--	9.9k
AIT-QA	✗	--	--	515	1.9k
StatcanRetrieval	✗	--	--	870	5.9k
watsonxDocsQA	✗	--	--	30	1.1k

Citation

If you use TableIR Eval: Table-Text IR Evaluation Collection, please cite:

@misc{doshi2026tableir,
  title        = {TableIR Eval: Table-Text IR Evaluation Collection},
  author       = {Doshi, Meet and Boni, Odellia and Kumar, Vishwajeet and Sen, Jaydeep and Joshi, Sachindra},
  year         = {2026},
  institution  = {IBM Research},
  howpublished = {https://huggingface.co/collections/ibm-research/table-text-ir-evaluation},
  note         = {Hugging Face dataset collection}
}

All credit goes to original authors. Please cite their work:

@article{Nan2021FeTaQAFT,
  title={FeTaQA: Free-form Table Question Answering},
  author={Nan, Linyong and Hsieh, Chiachun and Mao, Ziming and Lin, Xi Victoria and Verma, Neha and Zhang, Rui and Kryściński, Wojciech and Schoelkopf, Hailey and Kong, Riley and Tang, Xiangru and Mutuma, Mutethia and Rosand, Ben and Trindade, Isabel and Bandaru, Renusree and Cunningham, Jacob and Xiong, Caiming and Radev, Dragomir},
  journal={Transactions of the Association for Computational Linguistics},
  year={2022},
  volume={10},
  pages={35-49}
}