UDA Natural Questions — NQ QA (orgrctera/uda_nq_qa)

Overview

This dataset is the Natural Questions (NQ) slice of the UDA (Unstructured Document Analysis) benchmark, packaged for retrieval-oriented evaluation: 2,477 question–answer instances grounded in Wikipedia articles, with gold long and short answers plus document identifiers.

Natural Questions (Kwiatkowski et al., TACL 2019) is a large-scale benchmark built from real anonymized Google Search queries. Annotators were shown a question and a full Wikipedia page (from top search results) and asked to mark long answers (typically paragraph-level evidence) and short answers (concise spans or entities) when supported by the page. The task reflects end-to-end open-domain QA: systems must cope with long, noisy web-style documents—not a single pre-selected paragraph.

UDA (Hui et al., NeurIPS 2024, Datasets & Benchmarks) is a suite for Retrieval-Augmented Generation (RAG) and LLM-based analysis over real-world documents in original messy formats (e.g. HTML/PDF), stressing parsing, chunking, and retrieval alongside generation. This Hub release aligns with UDA’s NQ QA configuration (sub_benchmark: nq_qa): each row is a retrieval task instance—pipelines must retrieve the right Wikipedia content (or equivalent passages) and ground predictions in that evidence when scoring answer quality.

Task

• Task type: Retrieval (within a RAG / document-analysis pipeline) for Natural Questions–style open-domain QA over Wikipedia-linked documents.
• Input: A natural-language question (input) as posed by real users.
• Supervision / reference: expected_output is a JSON string with gold long and short answers and a doc_url pointing at the Wikipedia revision used for annotation. metadata records UDA identifiers (sub_benchmark: nq_qa).

Evaluation typically combines retrieval quality (whether the model surfaces the correct article or passages) with answer correctness (e.g. long-answer F1 and short-answer F1 as in the original NQ leaderboard), consistent with UDA protocols that treat retrieval and parsing as first-class components.

Background

Natural Questions (NQ)

NQ targets question answering where the evidence is a full Wikipedia article rather than a single paragraph. Questions are naturally occurring; answers may require reading, coreference, and span selection at multiple granularities. The original work reports strong human agreement and uses long vs. short answer prediction as core tasks, making NQ a standard benchmark for open-domain and retrieval-augmented systems.

UDA benchmark

UDA aggregates 2,965 real-world documents and 29,590 expert-annotated Q&A pairs across domains and query types, evaluating design choices for LLM- and RAG-based document analysis. The NQ QA track in this release contains 2,477 examples in the default split, packaged with UDA metadata for integration with the broader benchmark.

Data fields

Column	Type	Description
input	string	Natural-language question (real user–style query).
expected_output	string	JSON with answers (long_answer, short_answer) and doc_url (Wikipedia page URL for the annotated revision).
metadata	struct	benchmark_name (uda_nq_qa), benchmark_type (uda), split, sub_benchmark (nq_qa), and value (JSON string with label_key, label_file, doc_url, q_uid).

Splits: Single split default with 2,477 examples.

Examples

The following rows are taken from the dataset (JSON in expected_output is formatted for readability).

Example 1 — long and short answers (Supreme Court)

• input: who determines the size of the supreme court
• expected_output:

{
  "answers": {
    "long_answer": "Article III of the United States Constitution does not specify the number of justices. The Judiciary Act of 1789 called for the appointment of six `` judges ''. Although an 1801 act would have reduced the size of the court to five members upon its next vacancy, an 1802 act promptly negated the 1801 act, legally restoring the court 's size to six members before any such vacancy occurred. As the nation 's boundaries grew, Congress added justices to correspond with the growing number of judicial circuits: seven in 1807, nine in 1837, and ten in 1863.",
    "short_answer": "Congress added"
  },
  "doc_url": "https://en.wikipedia.org//w/index.php?title=Supreme_Court_of_the_United_States&oldid=815925358"
}

Example 2 — long answer; empty short answer

• input: actress who plays victoria newman on young and the restless
• expected_output:

{
  "answers": {
    "long_answer": "On March 21, 2005, Heinle joined the cast of the CBS soap opera The Young and the Restless, as Victoria Newman, replacing the popular Heather Tom in the role. She won a Daytime Emmy Award for Outstanding Supporting Actress in a Drama Series in 2014 and again in 2015 for the role.",
    "short_answer": ""
  },
  "doc_url": "https://en.wikipedia.org//w/index.php?title=Amelia_Heinle&oldid=850793939"
}

metadata.value (structure, example):

{
  "label_key": "Amelia Heinle",
  "label_file": "nq_qa",
  "doc_url": "https://en.wikipedia.org//w/index.php?title=Amelia_Heinle&oldid=850793939",
  "q_uid": "6002803770874668212"
}

References

Natural Questions (foundational dataset)

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov. Natural Questions: A Benchmark for Question Answering Research. Transactions of the Association for Computational Linguistics (TACL), 2019.

• Paper: ACL Anthology Q19-1026
• Project / dataset: Google Research — Natural Questions
• Repository: google-research-datasets/natural-questions

Abstract (summary): Introduces a corpus of real search queries paired with Wikipedia pages; annotators mark long answers (paragraph-level) and short answers when present, enabling realistic open-domain QA evaluation with human upper bounds on long- and short-answer selection.

UDA (benchmark suite containing this NQ slice)

Yulong Hui, Yao Lu, Huanchen Zhang. UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis. NeurIPS 2024 (Datasets and Benchmarks Track). Also: arXiv:2406.15187.

• Abstract (short): Presents UDA with thousands of real-world documents and tens of thousands of expert-annotated Q&A pairs; evaluates LLM- and RAG-based document analysis and highlights parsing and retrieval design choices across domains.
• Code & resources: UDA-Benchmark
• Aggregated UDA QA reference: qinchuanhui/UDA-QA

Citation

If you use this dataset, please cite Natural Questions, UDA, and this dataset record as appropriate:

@article{kwiatkowski-etal-2019-natural,
  title   = {Natural Questions: A Benchmark for Question Answering Research},
  author  = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
  journal = {Transactions of the Association for Computational Linguistics},
  volume  = {7},
  year    = {2019},
  pages   = {452--466},
  doi     = {10.1162/tacl_a_00276},
  url     = {https://aclanthology.org/Q19-1026/}
}

@inproceedings{hui2024uda,
  title     = {UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis},
  author    = {Yulong Hui and Yao Lu and Huanchen Zhang},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track},
  year      = {2024}
}

License

The original Natural Questions data is released by Google under terms described in the official repository and project page. Use this dataset in compliance with those terms and with the UDA benchmark conditions. This Hub card is provided for documentation; verify licenses for your use case before redistribution or commercial use.