The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
“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.
In the citing paper
“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
“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]
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
Building Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523
WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1237 · Cited by 829
Learning surface text patterns for a Question Answering system
Ravichandran, D., & Hovy, E. H. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073083.1073092 · Cited by 815
MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
Richardson, M., Burges, C., & Renshaw, E. (2013) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1020 · Cited by 778
Building a question answering test collection
Voorhees, E., & Tice, D. M. (2000) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/345508.345577 · Cited by 670
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., & Mikolov, T. (2015) · arXiv · doi:10.48550/arxiv.1502.05698 · Cited by 670
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Chen, D., Bolton, J., & Manning, C. D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1223 · Cited by 494
Cited by
Sorted by citations
Items 601–700 of 1,109
Datasets: A Community Library for Natural Language ProcessingPreprint
Lhoest, Q., del Moral, A. V., Jernite, Y., Thakur, A., von Platen, P., Patil, S. S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.02846 · Cited by 133
Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?
Ravichander, A., Belinkov, Y., & Hovy, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.295 · Cited by 132
Talking-Heads AttentionPreprint
Shazeer, N., Lan, Z., Cheng, Y., Ding, N., & Hou, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02436 · Cited by 132
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningPreprint
Aribandi, V., Tay, Y., Schuster, T., Rao, J., Zheng, H., Mehta, S. V., Zhuang, H., Tran, V. Q., Bahri, D., Ni, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10952 · Cited by 132
A survey on large language models with multilingualism: recent advances and new frontiers
Huang, K., Mo, F., Zhang, X., Li, H., Li, Y., Zhang, Y., yi, W., Mao, Y., Liu, J., Xu, Y., et al. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11534-5 · Cited by 131
Pre-training via ParaphrasingPreprint
Lewis, M., Ghazvininejad, M., Ghosh, G., Aghajanyan, A., Wang, S. I., & Zettlemoyer, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15020 · Cited by 131
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
Bartolo, M., Roberts, A., Welbl, J., Riedel, S., & Stenetorp, P. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00338 · Cited by 130
FACT: FFN-Attention Co-optimized Transformer Architecture with Eager Correlation Prediction
Qin, Y., Wang, Y., Deng, D., Zhao, Z., Yang, X., Liu, L., Wei, S., Hu, Y., & Yin, S. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589057 · Cited by 130
Large Language Model Instruction Following: A Survey of Progresses and Challenges
Lou, R., Zhang, K., & Yin, W. (2024) · Computational Linguistics · doi:10.1162/coli_a_00523 · Cited by 133
Behavior Transformers: Cloning k Modes with One Stone
Shafiullah, N. M. M., Cui, Z., Altanzaya, A., & Pinto, L. (2022) · neural information processing systems · doi:10.52202/068431-1668 · Cited by 130
How to Train Data-Efficient LLMsPreprint
Sachdeva, N., Coleman, B., Kang, W.-C., Ni, J., Hong, L., Ed, H., Caverlee, J., McAuley, J., & Cheng, D. Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.09668 · Cited by 133
Challenges in Automated Debiasing for Toxic Language Detection
Zhou, X., Sap, M., Swayamdipta, S., Choi, Y., & Smith, N. A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.274 · Cited by 129
BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Kanakarajan, K. R., Kundumani, B., & Sankarasubbu, M. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.16 · Cited by 131
Lavanya, P., & Sasikala, E. (2021) · doi:10.1109/icspc51351.2021.9451752 · Cited by 129
StructuralLM: Structural Pre-training for Form Understanding
Li, C., Bi, B., Yan, M., Wang, W., Huang, S., Huang, F., & Si, L. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.493 · Cited by 129
Zero-Shot Video Question Answering Via Frozen Bidirectional Language Models
Yang, A., Miech, A., Šivic, J., Laptev, I., & Schmid, C. (2022) · neural information processing systems · doi:10.52202/068431-0010 · Cited by 129
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingPreprint
Antoun, W., Baly, F., & Hajj, H. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15516 · Cited by 129
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine LearningPreprint
Luccioni, A. S., & Hernández-García, Á. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.08476 · Cited by 129
Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks
Song, L., Wang, Z., Yu, M., Zhang, Y., Florian, R., & Gildea, D. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2982894 · Cited by 128
Emerging trends: A gentle introduction to fine-tuning
Church, K., Chen, Z., & Ma, Y. (2021) · Natural Language Engineering · doi:10.1017/s1351324921000322 · Cited by 129
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERTPreprint
Sun, L., Hashimoto, K., Yin, W., Asai, A., Li, J., Yu, P. S., & Xiong, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04985 · Cited by 128
Deep learning for drug–drug interaction extraction from the literature: a review
Zhang, T., Leng, J., & Liu, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz087 · Cited by 129
CharBERT: Character-aware Pre-trained Language Model
Ma, W., Cui, Y., Si, C., Liu, T., Wang, S., & Hu, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.4 · Cited by 127
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation
Passban, P., Wu, Y., Rezagholizadeh, M., & Liu, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17610 · Cited by 127
Lei, T., Zhang, Y., & Artzi, Y. (2017) · Cited by 126
GREEK-BERT: The Greeks visiting Sesame Street
Koutsikakis, J., Chalkidis, I., Malakasiotis, P., & Androutsopoulos, I. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411440 · Cited by 126
Open Domain Question Answering over Tables via Dense Retrieval
Herzig, J., Müller, T., Krichene, S., & Eisenschlos, J. M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.43 · Cited by 126
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
Chen, Y., Fu, Q., Yuan, Y., Wen, Z., Fan, G., Liu, D., Zhang, D., Li, Z., & Xiao, Y. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614905 · Cited by 128
Learning Intrinsic Sparse Structures within Long Short-Term MemoryPreprint
Wen, W., He, Y., Rajbhandari, S., Zhang, M., Wang, W., Liu, F., Hu, B., Chen, Y., & Li, H. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05027 · Cited by 126
A Mutual Information Maximization Perspective of Language Representation LearningPreprint
Kong, L., d’Autume, C. D. M., Wang, L., Yu, L., Dai, Z., & Yogatama, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08350 · Cited by 129
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
Saha, A., Aralikatte, R., Khapra, M. M., & Sankaranarayanan, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1156 · Cited by 125
Neural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835533 · Cited by 125
A survey on question answering systems over linked data and documents
Dimitrakis, E., Sgontzos, K., & Tzitzikas, Y. (2019) · Journal of Intelligent Information Systems · doi:10.1007/s10844-019-00584-7 · Cited by 125
Neural Approaches to Conversational Information Retrieval
Gao, J., Xiong, C., Bennett, P. N., & Craswell, N. (2023) · The information retrieval series · doi:10.1007/978-3-031-23080-6 · Cited by 127
Rethinking embedding coupling in pre-trained language modelsPreprint
Chung, H. W., Févry, T., Tsai, H., Johnson, M., & Ruder, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12821 · Cited by 125
UNQOVERing Stereotyping Biases via Underspecified Questions
Li, T., Khashabi, D., Khot, T., Sabharwal, A., & Srikumar, V. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.311 · Cited by 124
Delving Deeper into Anti-Aliasing in ConvNets
Zou, X., Xiao, F., Yu, Z., Li, Y., & Lee, Y. J. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01672-y · Cited by 125
Qin, Y., Lin, Y., Takanobu, R., Liu, Z., Li, P., Ji, H., Huang, M., Sun, M., & Zhou, J. (2021) · doi:10.18653/v1/2021.acl-long.260 · Cited by 125
Contrastive self-supervised learning: review, progress, challenges and future research directions
Kumar, P., Rawat, P., & Chauhan, S. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00245-6 · Cited by 125
Parameter-Efficient Model Adaptation for Vision Transformers
He, X., Li, C., Zhang, P., Yang, J., & Wang, X. E. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25160 · Cited by 124
DRCD: a Chinese Machine Reading Comprehension DatasetPreprint
Shao, C.-C., Liu, T., Lai, Y., Tseng, Y., & Tsai, S. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00920 · Cited by 124
Recent Advances in Neural Question GenerationPreprint
Pan, L., Lei, W., Chua, T., & Kan, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.08949 · Cited by 124
Teach Me to Explain: A Review of Datasets for Explainable Natural Language ProcessingPreprint
Wiegreffe, S., & Marasović, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12060 · Cited by 128
Automatic question generation and answer assessment: a survey
Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-021-00151-1 · Cited by 123
Samant, R. M., Bachute, M. R., Gite, S., & Kotecha, K. (2022) · IEEE Access · doi:10.1109/access.2022.3149798 · Cited by 123
Chao, G.-L., & Lane, I. R. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1355 · Cited by 123
Shi, J., Cao, S., Hou, L., Li, J., & Zhang, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.341 · Cited by 123
Credit default prediction from user-generated text in peer-to-peer lending using deep learning
Kriebel, J., & Stitz, L. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.12.024 · Cited by 123
DataPerf: Benchmarks for Data-Centric AI DevelopmentPreprint
Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.10062 · Cited by 123
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Bansal, T., Jha, R., & McCallum, A. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.448 · Cited by 125
Brown, A., Roman, M., & Devereux, B. J. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9120320 · Cited by 127
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
Scialom, T., Lamprier, S., Piwowarski, B., & Staiano, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1320 · Cited by 121
Neural Domain Adaptation for Biomedical Question Answering
Wiese, G. K., Weissenborn, D., & Neves, M. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1029 · Cited by 121
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
Lin, H., Lu, Y., Han, X., & Sun, L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1511 · Cited by 121
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Zhang, S., & Bansal, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1253 · Cited by 121
Larger-Scale Transformers for Multilingual Masked Language Modeling
Goyal, N., Du, J., Ott, M., Anantharaman, G., & Conneau, A. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.4 · Cited by 121
How to Train BERT with an Academic Budget
Izsak, P., Berchansky, M., & Levy, O. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.831 · Cited by 120
S-Net: From Answer Extraction to Answer Synthesis for Machine Reading Comprehension
Tan, C., Wei, F., Yang, N., Du, B., Lv, W., & Zhou, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12035 · Cited by 120
Lan, W., & Xu, W. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04330 · Cited by 120
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksPreprint
Hui, L., & Belkin, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07322 · Cited by 120
The life cycle of large language models in education: A framework for understanding sources of bias
Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13505 · Cited by 119
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Fabbri, A., Wu, C.-S., Liu, W., & Xiong, C. (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.187 · Cited by 119
Hidden Backdoors in Human-Centric Language Models
Li, S., Liu, H., Dong, T., Zhao, B. Z. H., Xue, M., Zhu, H., & Lu, J. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3484576 · Cited by 119
Dave, S., Baghdadi, R., Nowatzki, T., Avancha, S., Shrivastava, A., & Li, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3098483 · Cited by 120
A building regulation question answering system: A deep learning methodology
Zhong, B., He, W., Huang, Z., Love, P. E. D., Tang, J., & Luo, H. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101195 · Cited by 119
Text mining approaches for dealing with the rapidly expanding literature on COVID-19
Wang, L. L., & Lo, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa296 · Cited by 119
CPM: A large-scale generative Chinese Pre-trained language model
Zhang, Z., Han, X., Zhou, H., Ke, P., Gu, Y., Ye, D., Qin, Y., Su, Y., Ji, H., Guan, J., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.07.001 · Cited by 119
SuperCLUE: A Comprehensive Chinese Large Language Model BenchmarkPreprint
Xu, L. J., Li, A., Zhu, L., Xue, H., Zhu, C., Zhao, K., He, H., Zhang, X., Kang, Q., & Lan, Z. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.15020 · Cited by 128
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Zhao, Y., Li, Y., Li, C., & Zhang, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.454 · Cited by 118
Doddapaneni, S., Aralikatte, R., Ramesh, G., Goyal, S., Khapra, M. M., Kunchukuttan, A., & Kumar, P. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.693 · Cited by 118
Fine-tuned Language Models are Continual Learners
Scialom, T., Chakrabarty, T., & Muresan, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.410 · Cited by 118
A Survey on Green Deep LearningPreprint
Xu, J., Zhou, W., Fu, Z., Zhou, H., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05193 · Cited by 118
Improving the Robustness of Question Answering Systems to Question Paraphrasing
Gan, W. C., & Ng, H. T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1610 · Cited by 117
A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets
Zeng, C., Li, S., Li, Q., Hu, J., & Hu, J. (2020) · Applied Sciences · doi:10.3390/app10217640 · Cited by 117
Kim, B., Kim, H., Lee, S., Lee, G., Kwak, D., Hyeon, J. D., Park, S., Kim, S., Kim, S., Seo, D., et al. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.274 · Cited by 120
von der Mosel, J., Trautsch, A., & Herbold, S. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3178469 · Cited by 117
Trojaning Language Models for Fun and Profit
Zhang, X., Zhang, Z., Ji, S., & Wang, T. (2021) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp51992.2021.00022 · Cited by 117
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Goyal, S., Choudhury, A. R., Raje, S. M., Chakaravarthy, V. T., Sabharwal, Y., & Verma, A. (2020) · arXiv · doi:10.48550/arxiv.2001.08950 · Cited by 117
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations · doi:10.18653/v1/d19-3002 · Cited by 116
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge
Ostermann, S., Roth, M., Modi, A., Thater, S., & Pinkal, M. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1119 · Cited by 116
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives
Ravichander, A., Black, A. W., Wilson, S., Norton, T. B., & Sadeh, N. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1500 · Cited by 116
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Ansell, A., Ponti, E. M., Korhonen, A., & Vulić, I. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.125 · Cited by 119
An LLM can Fool Itself: A Prompt-Based Adversarial AttackPreprint
Xu, X., Kong, K., Liu, N., Cui, L., Wang, D., Zhang, J., & Kankanhalli, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.13345 · Cited by 116
Self-citation(2021) · doi:10.18653/v1/2021.naacl-main.46 · Cited by 115
Mavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 119
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Iandola, F., Shaw, A., Krishna, R., & Keutzer, K. (2020) · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing · doi:10.18653/v1/2020.sustainlp-1.17 · Cited by 114
Multiway Attention Networks for Modeling Sentence Pairs
Tan, C., Wei, F., Wang, W., Lv, W., & Zhou, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/613 · Cited by 114
Vashishth, S., Bhandari, M., Yadav, P., Rai, P., Bhattacharyya, C., & Talukdar, P. P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1320 · Cited by 114
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation
Li, K., Chen, C., Quan, X., Ling, Q. H., & Song, Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.631 · Cited by 116
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering
Gao, F., Ping, Q., Thattai, G., Reganti, A., Wu, Y. N., & Natarajan, P. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00501 · Cited by 114
FedPrompt: Communication-Efficient and Privacy-Preserving Prompt Tuning in Federated Learning
Zhao, H., Du, W., Li, F., Li, P., & Liu, G. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095356 · Cited by 114
Shuster, K., Komeili, M., Adolphs, L., Roller, S., Szlam, A. D., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.27 · Cited by 118
GUICourse: From General Vision Language Model to Versatile GUI Agent
Chen, W., Cui, J., Hu, J., Qin, Y., Fang, J., Zhao, Y. M., Wang, C., Liu, J., Chen, G., Huo, Y., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1065 · Cited by 114
Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., & Abend, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.619 · Cited by 113
“…research are to apply Q 2 in additional tasks where factual consistency is essential, such as automated fact-checking (Thorne and Vlachos, 2018), and to use its evaluation signal to improve the factual consistency of generation models as proposed by Rashkin et al. (2021) or Nan et al. (2021).”
BackgroundMethodWarstadt, A., Zhang, Y., Li, H.-S., Liu, H., & Bowman, S. R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.16 · Cited by 115
Rethinking search: making domain experts out of dilettantes
Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112
Tay, Y., Wang, S., Luu, A. T., Fu, J., Phan, M. C., Yuan, X., Rao, J., Hui, S. C., & Zhang, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1486 · Cited by 112
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 ›
Are you an author, or do you see a problem with this page? Request a review
