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# Who cites «How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks»

133 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/kaushik-2018-how-much-reading-does-reading/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kaushik-2018-how-much-reading-does-reading-2/)

1. [Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference](https://aclanthology.org/2025.coling-main.389/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
2. [Elements of World Knowledge ( EWoK ): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models](https://aclanthology.org/2025.tacl-1.57/) *— 2025*
3. [Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://aclanthology.org/2024.acl-long.555/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Desiderata For The Context Use Of Question Answering Systems](https://aclanthology.org/2024.eacl-long.47/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models](https://aclanthology.org/2024.lrec-main.704/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
6. [Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference](https://arxiv.org/abs/2410.08996) *— 2024 · arXiv:2410.08996*
7. [Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals](https://aclanthology.org/2024.findings-emnlp.205/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
8. [Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries](https://arxiv.org/abs/2409.12640) *— 2024 · arXiv:2409.12640*
9. [RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting](https://arxiv.org/abs/2412.07675) *— 2024 · arXiv:2412.07675*
10. [Towards Faithful Model Explanation in NLP: A Survey](https://aclanthology.org/2024.cl-2.6/) *— 2024 · Computational Linguistics*
11. [大语言模型故事理解能力评价数据集(Benchmarking story comprehension ability of large language model)](https://aclanthology.org/2024.ccl-1.60/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
12. [DISCO: Distilling Counterfactuals with Large Language Models](https://aclanthology.org/2023.acl-long.302/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Evaluation for Change](https://aclanthology.org/2023.findings-acl.522/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
14. [Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases](https://aclanthology.org/2023.findings-acl.833/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
15. [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective](https://aclanthology.org/2023.findings-acl.806/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
16. [Investigating UD Treebanks via Dataset Difficulty Measures](https://aclanthology.org/2023.eacl-main.76/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
17. [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://aclanthology.org/2023.findings-acl.202/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
18. [Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models](https://aclanthology.org/2023.findings-acl.237/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
19. [Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future](https://aclanthology.org/2023.emnlp-main.276/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
20. [Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya](https://aclanthology.org/2023.acl-long.661/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [SkillQG: Learning to Generate Question for Reading Comprehension Assessment](https://aclanthology.org/2023.findings-acl.870/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
22. [What's the Meaning of Superhuman Performance in Today's NLU?](https://aclanthology.org/2023.acl-long.697/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [When and Why Does Bias Mitigation Work?](https://aclanthology.org/2023.findings-emnlp.619/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
24. [``World Knowledge'''' in Multiple Choice Reading Comprehension](https://aclanthology.org/2023.fever-1.5/) *— 2023 · Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)*
25. [A Few More Examples May Be Worth Billions of Parameters](https://aclanthology.org/2022.findings-emnlp.72/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
26. [A Generative Approach for Mitigating Structural Biases in Natural Language Inference](https://aclanthology.org/2022.starsem-1.16/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
27. [Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs](https://aclanthology.org/2022.emnlp-main.658/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
28. [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://aclanthology.org/2022.emnlp-main.598/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
29. [CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation](https://aclanthology.org/2022.acl-long.475/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models](https://aclanthology.org/2022.emnlp-main.438/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
31. [Challenges in Generalization in Open Domain Question Answering](https://aclanthology.org/2022.findings-naacl.155/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
32. [Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing](https://aclanthology.org/2022.emnlp-main.592/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
33. [Dyna-bAbI: unlocking bAbI''s potential with dynamic synthetic benchmarking](https://aclanthology.org/2022.starsem-1.9/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
34. [LogicNMR: Probing the Non-monotonic Reasoning Ability of Pre-trained Language Models](https://aclanthology.org/2022.findings-emnlp.265/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
35. [Machine Reading, Fast and Slow: When Do Models ``Understand'''' Language?](https://aclanthology.org/2022.coling-1.8/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
36. [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://aclanthology.org/2022.acl-long.454/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [On the Robustness of Reading Comprehension Models to Entity Renaming](https://aclanthology.org/2022.naacl-main.37/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
38. [Partial-input baselines show that NLI models can ignore context, but they don't.](https://aclanthology.org/2022.naacl-main.350/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
39. [State-of-the-art generalisation research in NLP: A taxonomy and review](https://arxiv.org/abs/2210.03050) *— 2022 · arXiv:2210.03050 · Nat Mach Intell 5, 1161-1174 (2023)*
40. [Text-based NP Enrichment](https://aclanthology.org/2022.tacl-1.44/) *— 2022 · Transactions of the Association for Computational Linguistics*
41. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning](https://aclanthology.org/2021.emnlp-main.713/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
43. [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://aclanthology.org/2021.emnlp-main.819/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
44. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
45. [Benchmarking Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2021.findings-acl.56/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
46. [COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences](https://aclanthology.org/2021.findings-acl.78/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
47. [Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval](https://aclanthology.org/2021.acl-long.118/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
48. [Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI](https://aclanthology.org/2021.emnlp-main.303/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
49. [DynaSent: A Dynamic Benchmark for Sentiment Analysis](https://aclanthology.org/2021.acl-long.186/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
50. [Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models](https://aclanthology.org/2021.emnlp-main.445/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
51. [Hurdles to Progress in Long-form Question Answering](https://arxiv.org/abs/2103.06332) *— 2021 · arXiv:2103.06332*
52. [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://aclanthology.org/2021.findings-emnlp.350/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
53. [On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study](https://aclanthology.org/2021.acl-long.517/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
54. [On the Interplay Between Fine-tuning and Composition in Transformers](https://aclanthology.org/2021.findings-acl.201/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
55. [Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://aclanthology.org/2021.eacl-main.295/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
56. [Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets](https://aclanthology.org/2021.eacl-main.86/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
57. [Robust Question Answering Through Sub-part Alignment](https://aclanthology.org/2021.naacl-main.98/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
58. [Sensitivity as a Complexity Measure for Sequence Classification Tasks](https://aclanthology.org/2021.tacl-1.53/) *— 2021 · Transactions of the Association for Computational Linguistics*
59. [Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.113/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
60. [Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning](https://aclanthology.org/2021.ranlp-1.148/) *— 2021 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)*
61. [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://aclanthology.org/2021.acl-long.98/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
62. [Why Machine Reading Comprehension Models Learn Shortcuts?](https://aclanthology.org/2021.findings-acl.85/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
63. [基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)](https://aclanthology.org/2021.ccl-1.41/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
64. [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://aclanthology.org/2020.lrec-1.660/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
65. [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://aclanthology.org/2020.acl-main.441/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
66. [Are Pretrained Language Models Symbolic Reasoners over Knowledge?](https://aclanthology.org/2020.conll-1.45/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
67. [Are we Estimating or Guesstimating Translation Quality?](https://aclanthology.org/2020.acl-main.558/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
68. [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://aclanthology.org/2020.tacl-1.43/) *— 2020*
69. [Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models](https://arxiv.org/abs/2005.14709) *— 2020 · arXiv:2005.14709*
70. [Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data](https://aclanthology.org/2020.findings-emnlp.76/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
71. [Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition](https://aclanthology.org/2020.emnlp-main.590/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
72. [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://arxiv.org/abs/2012.05345) *— 2020 · arXiv:2012.05345 · Patterns, Volume 2, Issue 11, 100336. 2021*
73. [Do We Need to Create Big Datasets to Learn a Task?](https://aclanthology.org/2020.sustainlp-1.23/) *— 2020 · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing*
74. [End-to-End Bias Mitigation by Modelling Biases in Corpora](https://aclanthology.org/2020.acl-main.769/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
75. [How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking](https://aclanthology.org/2020.emnlp-main.262/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
76. [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/2004.07347) *— 2020 · arXiv:2004.07347*
77. [Intrinsic Evaluation of Summarization Datasets](https://aclanthology.org/2020.emnlp-main.649/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
78. [MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering](https://aclanthology.org/2020.emnlp-main.63/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
79. [On the Cross-lingual Transferability of Monolingual Representations](https://aclanthology.org/2020.acl-main.421/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
80. [On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT](https://aclanthology.org/2020.starsem-1.10/) *— 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics*
81. [Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering](https://aclanthology.org/2020.emnlp-main.11/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
82. [Shortcut Learning in Deep Neural Networks](https://arxiv.org/abs/2004.07780) *— 2020 · arXiv:2004.07780*
83. [The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions](https://aclanthology.org/2020.emnlp-main.659/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
84. [The Sensitivity of Language Models and Humans to Winograd Schema Perturbations](https://aclanthology.org/2020.acl-main.679/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
85. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
86. [Towards Debiasing NLU Models from Unknown Biases](https://aclanthology.org/2020.emnlp-main.613/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
87. [Translation Artifacts in Cross-lingual Transfer Learning](https://aclanthology.org/2020.emnlp-main.618/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
88. [What Question Answering can Learn from Trivia Nerds](https://aclanthology.org/2020.acl-main.662/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
89. [What do we expect from Multiple-choice QA Systems?](https://aclanthology.org/2020.findings-emnlp.317/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
90. [Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects](https://aclanthology.org/W19-1801/) *— 2019 · Proceedings of the Second Workshop on Shortcomings in Vision and Language*
91. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
92. [Bend but Don't Break? Multi-Challenge Stress Test for QA Models](https://aclanthology.org/D19-5818/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
93. [CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text](https://aclanthology.org/D19-1458/) *— 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)*
94. [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) *— our card · 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
95. [Don''t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://aclanthology.org/P19-1084/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
96. [Evidence Sentence Extraction for Machine Reading Comprehension](https://aclanthology.org/K19-1065/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
97. [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://aclanthology.org/P19-1092/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
98. [Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://aclanthology.org/D19-5306/) *— 2019 · Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)*
99. [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://aclanthology.org/N19-1371/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
100. [Is It Dish Washer Safe? Automatically Answering ``Yes/No'' Questions Using Customer Reviews](https://aclanthology.org/N19-3001/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop*

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