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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Open accessAbstract
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
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In the citing paper
“Both metrics assume short, span-extractable answers, an assumption fundamentally violated by Think-class models that produce reasoning traces often running to several hundred tokens.”
From When Context Overwhelms: A Study of Long-Context vs. Retrieval-Based QA Under Noise (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“We use SQuAD 1.1 which excludes the unanswerable questions added in v.2.0 that are deliberately constructed to induce mistakes so they do not in practice cause confabulations to occur.”
From Detecting hallucinations in large language models using semantic entropy (2024) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The top-3 softmax-weighted configuration trades 1 . 6 F1 for multi-chunk fusion”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The empirically optimal k is therefore corpus-dependent: k =1 for single-paragraph evidence (SQuAD), k =2 -3 for multi-hop (HotpotQA).”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Because we use the released SQuAD reference answers verbatim and do not introduce model-assisted question generation, no additional human verification step is required.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We report token-level Exact Match (EM) and F1 against the gold answer set, computed via the official SQuAD v1.1 evaluation script. 1 The script applies the standard SQuAD normalizationlowercasing, stripping of punctuation, removal of articles ( a , an , the ), and whitespace tokenization-to both predictions and references before scoring”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [42]
The cited source says what the claim attributes to it.
In the citing paper
“We use SQuAD (Rajpurkar et al., 2016) and HotpotQA (Yang et al., 2018) as the base datasets, which are two widely used datasets in the field of QA and QG.”
From QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024) · cites this paper as Rajpurkar et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“Our results show that, following reinforcement learning (RL) training, finetuning on self-generated synthetic data improves question-answering performance on the no-passage-in-context variant of SQuAD [13] from 33.5% to 47.0%.”
From Self-Adapting Language Models (2025) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“Standard NLP models are often evaluated on large datasets of coherent text-based questions and answers [48, 49] or paired written dialogue [68].”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [49]
The cited source says what the claim attributes to it.
In the citing paper
“We evaluate on a randomly sampled 1 , 000 -question subset of the SQuAD v1.1 dev split using the official evaluation script”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“For tractability under the 19 tok/s edge-throughput inference configuration, we sample a fixed random subset of 1 , 000 questions from the SQuAD v1.1 dev split using a fixed random seed.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“Traditional question-answering (QA) benchmarks are typically designed to provide fixed answers to questions, as in datasets like SQuAD (Rajpurkar et al., 2016) and Natural Questions (Kwiatkowski et al., 2019).”
From DebateQA: Evaluating Question Answering on Debatable Knowledge (2026) · cites this paper as Rajpurkar et al., 2016
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“We found that existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [49]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“PRECOG top-1 matches in-context RAG to within 0 . 2 F1 and 0 . 2 EM on this subset”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The total chunk count is 2 , 067 , producing a state corpus of approximately 0 . 40 GB; the corresponding sentence-encoder key index occupies under 1 MB.”
From Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredIn the citing paper
“The type of questions to be generated range from the typical short answer span questions to multiple spans for question generation from the text [42, 47, 106, 107, 112].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [42]
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Few-Shot Self-Rationalization with Natural Language Prompts
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Universal Information Extraction as Unified Semantic Matching
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MIRACL : A Multilingual Retrieval Dataset Covering 18 Diverse Languages
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Greco, C. M., & Tagarelli, A. (2023) · Artificial Intelligence and Law · doi:10.1007/s10506-023-09374-7 · Cited by 87
Learning to Compute Word Embeddings On the FlyPreprint
Bahdanau, D., Bosc, T., Jastrzȩbski, S., Grefenstette, E., Vincent, P., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00286 · Cited by 87
Research community dynamics behind popular AI benchmarks
Martínez-Plumed, F., Barredo, P., hÉigeartaigh, S. Ó., & Hernández-Orallo, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00339-6 · Cited by 86
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension
Yin, Y., Song, Y., & Ming, Z. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1217 · Cited by 86
Large-Scale Transfer Learning for Natural Language Generation
Golovanov, S., Kurbanov, R., Nikolenko, S., Truskovskyi, K., Tselousov, A., & Wolf, T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1608 · Cited by 86
From static to dynamic word representations: a survey
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Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
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Coarse-to-Fine Query Focused Multi-Document Summarization
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Memory-efficient Transformers via Top-k Attention
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Text analysis using deep neural networks in digital humanities and information science
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InterBERT: Vision-and-Language Interaction for Multi-modal PretrainingPreprint
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Does Synthetic Data Make Large Language Models More Efficient?Preprint
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When Do You Need Billions of Words of Pretraining Data?
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Adversarially Regularising Neural
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KLEJ: Comprehensive Benchmark for Polish Language Understanding
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Short Text Clustering Algorithms, Application and Challenges: A Survey
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Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
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DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsPreprint
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With Little Power Comes Great Responsibility
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Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
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Dynamic Sparse Attention for Scalable Transformer Acceleration
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WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
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BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation ModelsPreprint
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LearningQ: A Large-Scale Dataset for Educational Question Generation
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Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
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Self-citationA Survey on Hardware Accelerators for Large Language Models
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Difficulty Controllable Generation of Reading Comprehension Questions
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MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal RadiographsPreprint
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Self-citationUnderstanding Dataset Design Choices for Multi-hop Reasoning
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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
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ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
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Metadata
- Authors
- Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 2383-2392
- OpenAlex
- W2963748441
- Cited by
- 6,208
- References
- 27
- Citations by year
- 2026: 335 · 2025: 492 · 2024: 506 · 2023: 1093 · 2022: 879 · 2021: 1127 · 2020: 858 · 2019: 592 · 2018: 254 · 2017: 62 · 2016: 3
- FWCI
- 163.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- question answering, reading comprehension, SQuAD, Wikipedia, dependency trees, constituency trees, logistic regression, human performance comparison, natural language understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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