ibm-research/AITQARetrieval

AIT-QA Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across test split(s), with corpus in 1 format(s): corpus.

Configs

Config	Description	Split(s)
default	Relevance judgments (qrels): qid, did, score	test
queries	Query IDs and text	test_queries
corpus	Plain text corpus: _id, title, text	corpus

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:

@misc{katsis2021aitqa,
      title={AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry}, 
      author={Yannis Katsis and Saneem Chemmengath and Vishwajeet Kumar and Samarth Bharadwaj and Mustafa Canim and Michael Glass and Alfio Gliozzo and Feifei Pan and Jaydeep Sen and Karthik Sankaranarayanan and Soumen Chakrabarti},
      year={2021},
      eprint={2106.12944},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}