ibm-research/MultiHierttRetrieval

MultiHierTT Retrieval

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

Configs

Config	Description	Split(s)
default	Relevance judgments (qrels): qid, did, score	dev
queries	Query IDs and text	dev_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:

@inproceedings{zhao-etal-2022-multihiertt,
    title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data",
    author = "Zhao, Yilun  and
      Li, Yunxiang  and
      Li, Chenying  and
      Zhang, Rui",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.454",
    pages = "6588--6600",
}