---
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…"
canonical: "https://research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/cited-by/"
---
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# Who cites «Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning»

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

1. [BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications](https://aclanthology.org/2026.findings-acl.1198/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning](https://aclanthology.org/2026.acl-long.2073/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [A Systematic Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
4. [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://aclanthology.org/2025.emnlp-main.293/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
5. [Aligning Black-box Language Models with Human Judgments](https://aclanthology.org/2025.findings-naacl.376/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
6. [Benchmarking Korean Idiom Understanding: A Comparative Analysis of Local and Global Models](https://aclanthology.org/2025.ranlp-1.156/) *— 2025 · Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era*
7. [Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning](https://aclanthology.org/2025.acl-long.1573/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Calibrating LLM Confidence by Probing Perturbed Representation Stability](https://aclanthology.org/2025.emnlp-main.530/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
9. [CausalEval: Towards Better Causal Reasoning in Language Models](https://aclanthology.org/2025.naacl-long.622/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
10. [Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs](https://aclanthology.org/2025.coling-main.703/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
11. [Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese](https://arxiv.org/abs/2502.12932) *— 2025 · arXiv:2502.12932*
12. [DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking](https://aclanthology.org/2025.findings-naacl.356/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
13. [FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema](https://aclanthology.org/2025.coling-main.731/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
14. [Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition](https://aclanthology.org/2025.coling-main.155/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
15. [Glider: Global and Local Instruction-Driven Expert Router](https://aclanthology.org/2025.emnlp-main.319/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
16. [GroundCocoa: A Benchmark for Evaluating Compositional & Conditional Reasoning in Language Models](https://aclanthology.org/2025.naacl-long.420/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
17. [JGU Mainz”s Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA](https://aclanthology.org/2025.wmt-1.89/) *— 2025 · Proceedings of the Tenth Conference on Machine Translation*
18. [KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning](https://aclanthology.org/2025.acl-long.1577/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [KazBench-KK: A Cultural-Knowledge Benchmark for Kazakh](https://aclanthology.org/2025.fieldmatters-1.4/) *— 2025 · Proceedings of the Fourth Workshop on NLP Applications to Field Linguistics*
20. [LLMs May Perform MCQA by Selecting the Least Incorrect Option](https://aclanthology.org/2025.coling-main.390/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
21. [MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models](https://aclanthology.org/2025.knowllm-1.6/) *— 2025 · Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)*
22. [NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
23. [On the Impact of Fine-Tuning on Chain-of-Thought Reasoning](https://aclanthology.org/2025.naacl-long.584/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
24. [PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning](https://aclanthology.org/2025.acl-long.1389/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [PerCoR: Evaluating Commonsense Reasoning in Persian via Multiple-Choice Sentence Completion](https://aclanthology.org/2025.ijcnlp-long.120/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
26. [Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles](https://aclanthology.org/2025.emnlp-main.1155/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
27. [Superpose Task-specific Features for Model Merging](https://aclanthology.org/2025.emnlp-main.210/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
28. [Towards Quantifying Commonsense Reasoning with Mechanistic Insights](https://aclanthology.org/2025.naacl-long.487/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
29. [UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions](https://aclanthology.org/2025.findings-acl.423/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
30. [Understanding Silent Data Corruption in LLM Training](https://aclanthology.org/2025.acl-long.996/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [\textit D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Models](https://aclanthology.org/2025.findings-acl.174/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
32. [AmazUtah_NLP at SemEval-2024 Task 9: A MultiChoice Question Answering System for Commonsense Defying Reasoning](https://aclanthology.org/2024.semeval-1.206/) *— 2024 · Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)*
33. [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://aclanthology.org/2024.findings-acl.334/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
34. [Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification](https://aclanthology.org/2024.findings-acl.618/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
35. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
36. [BEnQA: A Question Answering Benchmark for Bengali and English](https://aclanthology.org/2024.findings-acl.68/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
37. [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://aclanthology.org/2024.lrec-main.296/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
38. [CLOMO: Counterfactual Logical Modification with Large Language Models](https://aclanthology.org/2024.acl-long.593/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Calibration-Tuning: Teaching Large Language Models to Know What They Don”t Know](https://aclanthology.org/2024.uncertainlp-1.1/) *— 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)*
40. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
41. [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)*
42. [Digital Socrates: Evaluating LLMs through Explanation Critiques](https://aclanthology.org/2024.acl-long.302/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://aclanthology.org/2024.emnlp-main.799/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
44. [EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning](https://aclanthology.org/2024.findings-emnlp.125/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
45. [Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process](https://aclanthology.org/2024.emnlp-main.74/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
46. [Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues](https://aclanthology.org/2024.acl-long.90/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [Every Answer Matters: Evaluating Commonsense with Probabilistic Measures](https://aclanthology.org/2024.acl-long.29/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://aclanthology.org/2024.lrec-main.708/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
49. [InstructEval: Systematic Evaluation of Instruction Selection Methods](https://aclanthology.org/2024.findings-naacl.270/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
50. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
51. [Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering](https://aclanthology.org/2024.naacl-short.44/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
52. [KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge](https://aclanthology.org/2024.findings-acl.666/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
53. [LC4EE: LLMs as Good Corrector for Event Extraction](https://aclanthology.org/2024.findings-acl.715/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
54. [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://arxiv.org/abs/2406.18403) *— 2024 · arXiv:2406.18403*
55. [Large Language Models Must Be Taught to Know What They Don't Know](https://arxiv.org/abs/2406.08391) *— 2024 · arXiv:2406.08391*
56. [MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://aclanthology.org/2024.acl-long.224/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning](https://aclanthology.org/2024.naacl-long.235/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
58. [MLT-DR: Multi-Lingual/Task Demonstration Retrieval An Attempt towards Generalized Retriever for In-Context Learning](https://aclanthology.org/2024.mrl-1.27/) *— 2024 · Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)*
59. [McGill NLP Group Submission to the MRL 2024 Shared Task: Ensembling Enhances Effectiveness of Multilingual Small LMs](https://aclanthology.org/2024.mrl-1.28/) *— 2024 · Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)*
60. [Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning](https://arxiv.org/abs/2409.19075) *— 2024 · arXiv:2409.19075*
61. [Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?](https://aclanthology.org/2024.acl-long.304/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches?](https://aclanthology.org/2024.insights-1.18/) *— 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP*
63. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://aclanthology.org/2024.acl-long.65/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
64. [On Narrative Question Answering Skills](https://aclanthology.org/2024.naacl-short.73/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
65. [PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset](https://aclanthology.org/2024.findings-acl.599/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
66. [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://aclanthology.org/2024.emnlp-main.249/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
67. [SCA: Selective Compression Attention for Efficiently Extending the Context Window of Large Language Models](https://aclanthology.org/2024.findings-emnlp.358/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
68. [SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://aclanthology.org/2024.naacl-long.22/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
69. [SocREval: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation](https://aclanthology.org/2024.findings-naacl.175/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
70. [Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the US](https://aclanthology.org/2024.emnlp-main.532/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
71. [Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models](https://aclanthology.org/2024.findings-acl.81/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
72. [Text2Afford: Probing Object Affordance Prediction abilities of Language Models solely from Text](https://aclanthology.org/2024.conll-1.27/) *— 2024 · Proceedings of the 28th Conference on Computational Natural Language Learning*
73. [The Critique of Critique](https://aclanthology.org/2024.findings-acl.538/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
74. [Think Twice: Perspective-Taking Improves Large Language Models” Theory-of-Mind Capabilities](https://aclanthology.org/2024.acl-long.451/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
75. [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://aclanthology.org/2024.lrec-main.1481/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
76. [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://aclanthology.org/2024.findings-acl.844/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
77. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
78. [Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://aclanthology.org/2023.emnlp-demo.13/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
79. [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://arxiv.org/abs/2308.00002) *— 2023 · arXiv:2308.00002*
80. [Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering](https://aclanthology.org/2023.findings-eacl.87/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
81. [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
82. [Benchmarks for Automated Commonsense Reasoning: A Survey](https://arxiv.org/abs/2302.04752) *— 2023 · arXiv:2302.04752*
83. [Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models](https://aclanthology.org/2023.crac-main.5/) *— 2023 · Proceedings of the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)*
84. [CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models](https://aclanthology.org/2023.findings-eacl.23/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
85. [CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks](https://aclanthology.org/2023.emnlp-main.607/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
86. [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://arxiv.org/abs/2302.07926) *— 2023 · arXiv:2302.07926*
87. [Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://aclanthology.org/2023.findings-acl.752/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
88. [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) *— 2023 · arXiv:2306.09030*
89. [Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models?](https://aclanthology.org/2023.emnlp-main.143/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
90. [Don”t Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://aclanthology.org/2023.eacl-main.130/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
91. [Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt](https://aclanthology.org/2023.findings-emnlp.822/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
92. [Evaluating and Improving the Coreference Capabilities of Machine Translation Models](https://aclanthology.org/2023.eacl-main.69/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
93. [Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension](https://aclanthology.org/2023.emnlp-main.9/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
94. [Exploring the Benefits of Training Expert Language Models over Instruction Tuning](https://arxiv.org/abs/2302.03202) *— 2023 · arXiv:2302.03202*
95. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
96. [Few-shot Unified Question Answering: Tuning Models or Prompts?](https://aclanthology.org/2023.findings-emnlp.550/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
97. [FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning](https://aclanthology.org/2023.acl-long.454/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference](https://aclanthology.org/2023.findings-acl.30/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
99. [Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning](https://aclanthology.org/2023.findings-acl.148/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
100. [Instructive Dialogue Summarization with Query Aggregations](https://aclanthology.org/2023.emnlp-main.474/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*

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