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# Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning» · page 3

266 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/huang-2019-cosmos-qa-machine-reading-comprehension/)

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

1. [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://arxiv.org/abs/2002.01808) *— 2020 · arXiv:2002.01808*
2. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
3. [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) *— 2020 · arXiv:2009.03300*
4. [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://aclanthology.org/2020.acl-main.130/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
5. [Paragraph-level Commonsense Transformers with Recurrent Memory](https://arxiv.org/abs/2010.01486) *— 2020 · arXiv:2010.01486*
6. [ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning](https://aclanthology.org/2020.emnlp-main.85/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
7. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
8. [Scene Restoring for Narrative Machine Reading Comprehension](https://aclanthology.org/2020.emnlp-main.247/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
9. [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.327/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
10. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
11. [Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text](https://aclanthology.org/2020.emnlp-main.111/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
12. [Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning](https://aclanthology.org/2020.findings-emnlp.289/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
13. [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://aclanthology.org/2020.emnlp-main.185/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
14. [On Making Reading Comprehension More Comprehensive](https://aclanthology.org/D19-5815/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
15. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
16. adak-2024-text2afford-probing-object-affordance-prediction
17. adiba-2022-unsupervised-domain-adaptation-question-answering
18. alikhani-2025-percor-evaluating-commonsense-reasoning-persian
19. bansal-2023-few-shot-unified-question-answering
20. bezancon-2023-spqr-deft2023-similarite-sorbonne-pour
21. bruyn-2022-20q-overlap-free-world-knowledge
22. burg-2025-aligning-black-box-language-models
23. byun-2025-textit-d-gen-automatic-distractor
24. cao-2023-does-correctness-factual-knowledge-matter
25. chu-2024-navigate-through-enigmatic-labyrinth-survey
26. clinciu-2021-it-s-commonsense-isn-t
27. damirchi-2026-truth-as-trajectory-what-internal
28. do-2023-machine-reading-comprehension-vietnamese-customer
29. feng-2023-learning-predict-task-transferability-soft
30. fu-2024-meta-rtl-reinforcement-based-meta
31. gu-2024-digital-socrates-evaluating-llms-through
32. hao-2026-bizcompass-benchmarking-reasoning-capabilities-llms
33. hashimoto-2024-mlt-dr-multi-lingual-task
34. huang-2023-learning-easily-updated-general-purpose
35. jung-2024-is-prompt-transfer-always-effective
36. kalbaliyev-2024-narrative-question-answering-skills
37. keith-2020-uncertainty-over-uncertainty-investigating-assumptions
38. khanmohammadi-2025-calibrating-llm-confidence-probing-perturbed
39. kohli-2025-groundcocoa-benchmark-evaluating-compositional-conditional
40. li-2024-mcgill-nlp-group-submission-mrl
41. li-2025-glider-global-local-instruction-driven
42. lu-2025-piper-benchmarking-prompting-event-reasoning
43. mishra-2024-amazutah-nlp-semeval-2024-task
44. negheimish-2021-discrete-reasoning-templates-natural-language
45. omura-2022-improving-commonsense-contingent-reasoning-pseudo
46. pang-2023-sharpt-shared-latent-space-prompt
47. qin-2025-beyond-output-matching-bidirectional-alignment
48. qiu-2025-superpose-task-specific-features-model
49. ren-2022-specializing-pre-trained-language-models
50. saadi-2025-jgu-mainz-s-submission-wmt25
51. sosa-2020-seeing-world-through-text-evaluating
52. sui-2025-kristeva-close-reading-as-novel
53. sun-2022-improving-machine-reading-comprehension-contextualized
54. teo-2020-tr-semeval-2020-task-4
55. tu-2025-mlan-language-based-instruction-tuning
56. umbet-2025-kazbench-kk-cultural-knowledge-benchmark
57. uzunoglu-2024-paradise-evaluating-implicit-planning-skills
58. wang-2025-benchmarking-korean-idiom-understanding-comparative
59. yang-2022-are-visual-linguistic-models-commonsense
60. ye-2023-efficiently-enhancing-zero-shot-performance
61. yi-2022-different-tunes-played-equal-skill
62. yu-2023-jecc-commonsense-reasoning-tasks-derived
63. yu-2025-generating-commonsense-reasoning-questions-controllable
64. zhang-2022-hrca-advanced-multiple-choice-machine
65. zhao-2022-lite-unified-modeling-discriminative-reading
66. zheng-2024-sca-selective-compression-attention-efficiently

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