ai21labs/aggregative_questions

AI21-Hotels and AI21-WorldCup datasets

The AI21-Hotels and AI21-WorldCup datasets were created to support research on aggregative question answering in open-book settings. Aggregative questions require retrieving information from a large set of documents and applying reasoning over the collected text snippets. For example, the question “What is the fewest number of total goals scored in any single World Cup?” cannot usually be answered by a single passage. Instead, one must gather the goals scored in each tournament and then reason to identify the minimum. These datasets were first introduced in the paper "Structured RAG for Answering Aggregative Questions", which describes their design and construction in detail.

🏨 AI21-Hotels dataset

The AI21-Hotels dataset was automatically generated, with booking-like hotel pages, paired with aggregative queries. The train set (in folder AI21-Hotels-train) includes 50 documents, and 138 queries. The evaluation set (in folder AI21-Hotels-eval) includes 350 documents, and 193 queries. An example document from AI21-Hotels train set is shown in the image below:

⚽ AI21-WorldCup dataset

The AI21-WorldCup dataset consists of 22 Wikipedia pages, each corresponding to one WorldCup tournament held between 1930 and 2022, and 88 automatically generated aggregative queries. To increase the difficulty, the main statistics table was removed from each document. Because there are only 22 tournaments in total, the dataset is provided as a single collection without separate training and evaluation splits.

📊 Statistics

Dataset	Split	# Docs	# Queries	Avg. Tokens / Doc
Hotels	Train	50	138	592
Hotels	Eval	350	193	596
WorldCup	---	22	88	18881

Languages

English

License

Datasets are released under the MIT license.

Citation

If you use these datasets, please cite:

@article{koshorek2025structured,
  title={Structured RAG for Answering Aggregative Questions},
  author={Koshorek, Omri and Granot, Niv and Alloni, Aviv and Admati, Shahar and Hendel, Roee and Weiss, Ido and Arazi, Alan and Cohen, Shay-Nitzan and Belinkov, Yonatan},
  journal={arXiv preprint arXiv:2511.08505},
  year={2025}
}