ibm-research/OTTQASmallRetrieval

OTT-QA Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across dev split(s), with corpus in 3 format(s): corpus_linearized, corpus_md, corpus_structure.

Configs

Config	Description	Split(s)
default	Relevance judgments (qrels): qid, did, score	dev
queries	Query IDs and text	dev_queries
corpus_linearized	Linearized table representation	corpus_linearized
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{chen2021ottqa,
  title={Open Question Answering over Tables and Text},
  author={Wenhu Chen, Ming-wei Chang, Eva Schlinger, William Wang, William Cohen},
  journal={Proceedings of ICLR 2021},
  year={2021}
}