---
title: "Who cites Cosmos QA: Machine Reading Comprehension with…"
description: "The papers of the Elamur Research library that cite «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning», found in their…"
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---
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# Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 2

266 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/cited-by/page/3/)

1. [JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://aclanthology.org/2023.findings-acl.713/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
2. [KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding](https://aclanthology.org/2023.findings-acl.743/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
3. [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://aclanthology.org/2023.emnlp-main.760/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
4. [Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix](https://aclanthology.org/2023.findings-emnlp.497/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
5. [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://aclanthology.org/2023.acl-long.659/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [Learning to Predict Task Transferability via Soft Prompt](https://aclanthology.org/2023.emnlp-main.546/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
7. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Machine Reading Comprehension for Vietnamese Customer Reviews: Task, Corpus and Baseline Models](https://aclanthology.org/2023.paclic-1.3/) *— 2023 · Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation*
9. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
10. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
11. [NormBank: A Knowledge Bank of Situational Social Norms](https://aclanthology.org/2023.acl-long.429/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer](https://aclanthology.org/2023.acl-short.24/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
13. [Pre-Training to Learn in Context](https://aclanthology.org/2023.acl-long.267/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Prompting with Pseudo-Code Instructions](https://aclanthology.org/2023.emnlp-main.939/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
15. [QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering](https://aclanthology.org/2023.findings-emnlp.1023/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
16. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
17. [ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://aclanthology.org/2023.emnlp-main.622/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
18. [SPQR@Deft2023: Similarité Sorbonne Pour les Systèmes de Question Réponse](https://aclanthology.org/2023.jeptalnrecital-deft.2/) *— 2023 · Actes de CORIA-TALN 2023. Actes du Défi Fouille de Textes@TALN2023*
19. [SharPT: Shared Latent Space Prompt Tuning](https://aclanthology.org/2023.findings-eacl.92/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
20. [Shepherd: A Critic for Language Model Generation](https://arxiv.org/abs/2308.04592) *— 2023 · arXiv:2308.04592*
21. [Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs](https://arxiv.org/abs/2311.17371) *— 2023 · arXiv:2311.17371*
22. [Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement](https://aclanthology.org/2023.trustnlp-1.28/) *— 2023 · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)*
23. [TIES-Merging: Resolving Interference When Merging Models](https://arxiv.org/abs/2306.01708) *— 2023 · arXiv:2306.01708*
24. [TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks](https://arxiv.org/abs/2310.00752) *— 2023 · arXiv:2310.00752*
25. [TaskWeb: Selecting Better Source Tasks for Multi-task NLP](https://aclanthology.org/2023.emnlp-main.680/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
26. [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/abs/2301.13688) *— 2023 · arXiv:2301.13688*
27. [The Web Can Be Your Oyster for Improving Language Models](https://aclanthology.org/2023.findings-acl.46/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
28. [Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models](https://arxiv.org/abs/2310.00836) *— 2023 · arXiv:2310.00836*
29. [Unified Demonstration Retriever for In-Context Learning](https://aclanthology.org/2023.acl-long.256/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Using contradictions improves question answering systems](https://aclanthology.org/2023.acl-short.72/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
31. [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://arxiv.org/abs/2312.09390) *— 2023 · arXiv:2312.09390*
32. [Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation](https://arxiv.org/abs/2306.05783) *— 2023 · arXiv:2306.05783*
33. [Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks](https://aclanthology.org/2023.findings-acl.246/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
34. [“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)*
35. [常识问答研究综述](https://jos.org.cn/jos/article/abstract/6913) *— 2023 · 软件学报 (Journal of Software)*
36. [20Q: Overlap-Free World Knowledge Benchmark for Language Models](https://aclanthology.org/2022.gem-1.46/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
37. [A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation](https://aclanthology.org/2022.findings-naacl.172/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
38. [A Review on Language Models as Knowledge Bases](https://arxiv.org/abs/2204.06031) *— 2022 · arXiv:2204.06031*
39. [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://aclanthology.org/2022.emnlp-main.812/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
40. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
41. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
42. [Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension](https://aclanthology.org/2022.findings-acl.82/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
43. [Are Visual-Linguistic Models Commonsense Knowledge Bases?](https://aclanthology.org/2022.coling-1.491/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
44. [Attention-Focused Adversarial Training for Robust Temporal Reasoning](https://aclanthology.org/2022.lrec-1.800/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
45. [CoSe-Co: Text Conditioned Generative CommonSense Contextualizer](https://aclanthology.org/2022.naacl-main.83/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
46. [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://arxiv.org/abs/2201.12438) *— 2022 · arXiv:2201.12438*
47. [ConTinTin: Continual Learning from Task Instructions](https://aclanthology.org/2022.acl-long.218/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://aclanthology.org/2022.acl-long.244/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
50. [Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning](https://aclanthology.org/2022.findings-emnlp.244/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
51. [DiscoSense: Commonsense Reasoning with Discourse Connectives](https://aclanthology.org/2022.emnlp-main.703/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
52. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
53. [EvEntS ReaLM: Event Reasoning of Entity States via Language Models](https://aclanthology.org/2022.emnlp-main.129/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
54. [HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method](https://aclanthology.org/2022.lrec-1.651/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
55. [Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks](https://aclanthology.org/2022.coling-1.68/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
56. [Improving In-Context Few-Shot Learning via Self-Supervised Training](https://aclanthology.org/2022.naacl-main.260/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
57. [Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge](https://aclanthology.org/2022.acl-long.598/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization](https://aclanthology.org/2022.emnlp-main.105/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
59. [Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning](https://aclanthology.org/2022.acl-long.5/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Leveraging Large Language Models for Multiple Choice Question Answering](https://arxiv.org/abs/2210.12353) *— 2022 · arXiv:2210.12353*
61. [Leveraging QA Datasets to Improve Generative Data Augmentation](https://aclanthology.org/2022.emnlp-main.660/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
62. [Lite Unified Modeling for Discriminative Reading Comprehension](https://aclanthology.org/2022.acl-long.594/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text](https://aclanthology.org/2022.findings-acl.127/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
64. [Logical Reasoning for Task Oriented Dialogue Systems](https://aclanthology.org/2022.ecnlp-1.10/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
65. [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://aclanthology.org/2022.coling-1.126/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
66. [Narrative Why-Question Answering: A Review of Challenges and Datasets](https://aclanthology.org/2022.gem-1.48/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
67. [On Curriculum Learning for Commonsense Reasoning](https://aclanthology.org/2022.naacl-main.72/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
68. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
69. [Probing Script Knowledge from Pre-Trained Models](https://aclanthology.org/2022.umios-1.10/) *— 2022 · Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)*
70. [QuALITY: Question Answering with Long Input Texts, Yes!](https://aclanthology.org/2022.naacl-main.391/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
71. [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://aclanthology.org/2022.findings-acl.168/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
72. [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://arxiv.org/abs/2212.07919) *— 2022 · arXiv:2212.07919*
73. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
74. [Retrieval Augmentation for Commonsense Reasoning: A Unified Approach](https://aclanthology.org/2022.emnlp-main.294/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
75. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
76. [Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning](https://aclanthology.org/2022.findings-naacl.169/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
77. [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://aclanthology.org/2022.findings-emnlp.416/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
78. [The Legal Argument Reasoning Task in Civil Procedure](https://aclanthology.org/2022.nllp-1.17/) *— 2022 · Proceedings of the Natural Legal Language Processing Workshop 2022*
79. [Toward Building a Language Model for Understanding Temporal Commonsense](https://aclanthology.org/2022.aacl-srw.3/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop*
80. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
81. [Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering](https://aclanthology.org/2022.emnlp-main.691/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
82. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
83. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
84. [Unsupervised Domain Adaptation on Question-Answering System with Conversation Data](https://aclanthology.org/2022.sigdial-1.42/) *— 2022 · Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
85. [Using Commonsense Knowledge to Answer Why-Questions](https://aclanthology.org/2022.emnlp-main.79/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
86. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
87. [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://arxiv.org/abs/2208.07998) *— 2022 · arXiv:2208.07998*
88. [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)*
89. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
90. [Zero-shot Event Causality Identification with Question Answering](https://aclanthology.org/2022.clib-1.13/) *— 2022 · Proceedings of the Fifth International Conference on Computational Linguistics in Bulgaria (CLIB 2022)*
91. [`Am I the Bad One”? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models](https://aclanthology.org/2022.lrec-1.28/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
92. [ALICE++: Adversarial Training for Robust and Effective Temporal Reasoning](https://aclanthology.org/2021.paclic-1.40/) *— 2021 · Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation*
93. [Automatic Story Generation: Challenges and Attempts](https://aclanthology.org/2021.nuse-1.8/) *— 2021 · Proceedings of the Third Workshop on Narrative Understanding*
94. [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*
95. [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.162/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
96. [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*
97. [Can Machines Learn Morality? The Delphi Experiment](https://arxiv.org/abs/2110.07574) *— 2021 · arXiv:2110.07574*
98. [Commonsense-Focused Dialogues for Response Generation: An Empirical Study](https://aclanthology.org/2021.sigdial-1.13/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
99. [Comparing Test Sets with Item Response Theory](https://aclanthology.org/2021.acl-long.92/) *— 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)*
100. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*

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