mlsanityData to 7 Oct 2026

SQuAD: 100,000+ Questions for Machine Comprehension of Text

arXiv 1606.05250 ↗First posted 16 Jun 2016cs.CLcited by 4,115

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Cited by 253 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com

Key facts. Cited by 4,115 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2016.

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Citing papers per year: 2020: 3; 2021: 700; 2022: 709; 2023: 734; 2024: 773; 2025: 702; 2026: 494.

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