mlsanityData to 7 Oct 2026

NewsQA: A Machine Comprehension Dataset

arXiv 1611.09830 ↗First posted 29 Nov 2016cs.CL cs.AIcited by 330

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Cited by 27 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at https://datasets.maluuba.com/NewsQA.

Key facts. Cited by 330 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Nov 2016.

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Citing papers per year: 2021: 76; 2022: 89; 2023: 64; 2024: 50; 2025: 31; 2026: 20.

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  2. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
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  6. 2021QuestEval: Summarization Asks for Fact-based Evaluationcited by 241
  7. 2023Do Large Language Models Know What They Don't Know?cited by 238
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  9. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  10. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  11. 2021Mind the Gap: Assessing Temporal Generalization in Neural Language Modelscited by 226
  12. 2022LinkBERT: Pretraining Language Models with Document Linkscited by 209
38 more
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  4. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  5. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  6. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  7. 2021KLUE: Korean Language Understanding Evaluationcited by 113
  8. 2024DsDm: Model-Aware Dataset Selection with Datamodelscited by 103
  9. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  10. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  11. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  12. 2025HalluLens: LLM Hallucination Benchmarkcited by 88
  13. 2022ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Promptscited by 85
  14. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 85
  15. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  16. 2022Clotho-AQA: A Crowdsourced Dataset for Audio Question Answeringcited by 84
  17. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 82
  18. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  19. 2022GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspectivecited by 72
  20. 2022Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settingscited by 66
  21. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  22. 2021Few-Shot Question Answering by Pretraining Span Selectioncited by 62
  23. 2021What to Pre-Train on? Efficient Intermediate Task Selectioncited by 62
  24. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  25. 2022UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Trainingcited by 57
  26. 2021Improving Factual Consistency of Abstractive Summarization via Question Answeringcited by 55
  27. 2021Comparing Test Sets with Item Response Theorycited by 55
  28. 2023InstructRetro: Instruction Tuning post Retrieval-Augmented Pretrainingcited by 53
  29. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 53
  30. 2021Zero-Shot Dialogue State Tracking via Cross-Task Transfercited by 52
  31. 2021Knowing More About Questions Can Help: Improving Calibration in Question Answeringcited by 47
  32. 2022Read before Generate! Faithful Long Form Question Answering with Machine Readingcited by 47
  33. 2023Universal Information Extraction as Unified Semantic Matchingcited by 47
  34. 2021Keep it Simple: Unsupervised Simplification of Multi-Paragraph Textcited by 45
  35. 2024Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Modelscited by 44
  36. 2024Evaluating Copyright Takedown Methods for Language Modelscited by 42
  37. 2023Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answeringcited by 41
  38. 2021TR-BERT: Dynamic Token Reduction for Accelerating BERT Inferencecited by 40

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