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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
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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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Thomason, J., Murray, M., Cakmak, M., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1907.04957 · Cited by 173
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Dasigi, P., Liu, N. F., Marasović, A., Smith, N. A., & Gardner, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1606 · Cited by 173
What Would Elsa Do? Freezing Layers During Transformer Fine-TuningPreprint
Lee, J., Tang, R., & Lin, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03090 · Cited by 173
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
Talmor, A., & Berant, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1485 · Cited by 172
On the effect of dropping layers of pre-trained transformer models
Sajjad, H., Dalvi, F., Durrani, N., & Nakov, P. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101429 · Cited by 185
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
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MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
Lin, Z., Madotto, A., Winata, G. I., & Fung, P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.273 · Cited by 171
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Lewis, P., Wu, Y., Liu, L., Minervini, P., Küttler, H., Piktus, A., Stenetorp, P., & Riedel, S. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00415 · Cited by 171
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
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Wang, W., Yan, M., & Wu, C. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1158 · Cited by 170
Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities
Morshed, M. G., Sultana, T., Alam, A., & Lee, Y.-K. (2023) · Sensors · doi:10.3390/s23042182 · Cited by 170
Gmail Smart Compose: Real-Time Assisted Writing
Chen, M. X., Lee, B. N., Bansal, G., Cao, Y., Zhang, S., Lu, J., Tsay, J., Wang, Y., Dai, A. M., Chen, Z., et al. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330723 · Cited by 169
MetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169
Commonsense for Generative Multi-Hop Question Answering Tasks
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Arabic Offensive Language on Twitter: Analysis and ExperimentsPreprint
Mubarak, H., Rashed, A. A.-A., Darwish, K. M., Samih, Y., & Abdelalí, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02192 · Cited by 170
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
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Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168
BioMegatron: Larger Biomedical Domain Language Model
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Towards a Robust Deep Neural Network against Adversarial Texts: A Survey
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Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model
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Making Neural QA as Simple as Possible but not Simpler
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KronA: Parameter-Efficient Tuning with Kronecker Adapter
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Did the Model Understand the Question?
Mudrakarta, P. K., Taly, A., Sundararajan, M., & Dhamdhere, K. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1176 · Cited by 166
JEC-QA: A Legal-Domain Question Answering Dataset
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Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
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Improving Post Training Neural Quantization: Layer-wise Calibration and Integer ProgrammingPreprint
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A Recurrent BERT-based Model for Question Generation
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Adversarial Filters of Dataset BiasesPreprint
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Inspecting and Editing Knowledge Representations in Language ModelsPreprint
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Machine Comprehension by Text-to-Text Neural Question Generation
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Context-Aware Multi-View Summarization Network for Image-Text Matching
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Unifying Vision, Text, and Layout for Universal Document Processing
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Open-Retrieval Conversational Question Answering
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Towards Exploiting Background Knowledge for Building Conversation Systems
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Cosmos QA: Machine Reading Comprehension with Contextual Commonsense ReasoningPreprint
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Can AI-Generated Text be Reliably Detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11156 · Cited by 810
“Using the PEGASUS-based paraphraser, the detector’s accuracy drops from 97% to 80% with only a trade-off of 3.5 in perplexity score (see Table 1).”
MethodInfluentialKnowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Agarwal, O., Ge, H., Shakeri, S., & Al-Rfou, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.278 · Cited by 161
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
Cai, H., Lin, J., Lin, Y., Liu, Z., Tang, H., Wang, H., Zhu, L., & Han, S. (2022) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3486618 · Cited by 162
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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