---
title: "Who cites RACE: Large-scale ReAding Comprehension Dataset…"
description: "The papers of the Elamur Research library that cite «RACE: Large-scale ReAding Comprehension Dataset From Examinations», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/4/"
---
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# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 4

619 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/lai-2017-race-large-scale-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/3/) · **4** · [5](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/5/) · [6](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/) · [7](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/)

1. [SharPT: Shared Latent Space Prompt Tuning](https://aclanthology.org/2023.findings-eacl.92/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
2. [Shepherd: A Critic for Language Model Generation](https://arxiv.org/abs/2308.04592) *— 2023 · arXiv:2308.04592*
3. [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*
4. [Skywork: A More Open Bilingual Foundation Model](https://arxiv.org/abs/2310.19341) *— 2023 · arXiv:2310.19341*
5. [Sparse Neural Retrieval Model for Efficient Cross-Domain Retrieval-Based Question Answering](https://aclanthology.org/2023.paclic-1.82/) *— 2023 · Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation*
6. [Specialist or Generalist? Instruction Tuning for Specific NLP Tasks](https://aclanthology.org/2023.emnlp-main.947/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
7. [Statistically Profiling Biases in Natural Language Reasoning Datasets and Models](https://aclanthology.org/2023.findings-emnlp.299/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
8. [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675) *— 2023 · arXiv:2302.06675*
9. [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) *— 2023 · arXiv:2308.16884 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024*
10. [The Falcon Series of Open Language Models](https://arxiv.org/abs/2311.16867) *— 2023 · arXiv:2311.16867*
11. [The Generative AI Paradox: "What It Can Create, It May Not Understand](https://arxiv.org/abs/2311.00059) *— 2023 · arXiv:2311.00059*
12. [The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2023.eacl-main.113/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
13. [Toward Unified Controllable Text Generation via Regular Expression Instruction](https://aclanthology.org/2023.ijcnlp-main.1/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Training NLI Models Through Universal Adversarial Attack](https://aclanthology.org/2023.ccl-1.72/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
15. [Tunable Soft Prompts are Messengers in Federated Learning](https://aclanthology.org/2023.findings-emnlp.976/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
16. [UltraFeedback: Boosting Language Models with Scaled AI Feedback](https://arxiv.org/abs/2310.01377) *— 2023 · arXiv:2310.01377*
17. [Universal Self-Adaptive Prompting](https://aclanthology.org/2023.emnlp-main.461/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
18. [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)*
19. [WYWEB: A NLP Evaluation Benchmark For Classical Chinese](https://aclanthology.org/2023.findings-acl.204/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
20. [Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://aclanthology.org/2023.eacl-main.224/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
21. [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)*
22. [Won”t Get Fooled Again: Answering Questions with False Premises](https://aclanthology.org/2023.acl-long.309/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [“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)*
24. [An MRC Framework for Semantic Role Labeling](https://aclanthology.org/2022.coling-1.191/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
25. [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)*
26. [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*
27. [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) *— 2022 · arXiv:2208.03299*
28. [BBQ: A hand-built bias benchmark for question answering](https://aclanthology.org/2022.findings-acl.165/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
29. [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
30. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
31. [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*
32. [Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?](https://aclanthology.org/2022.coling-1.125/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
33. [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*
34. [Case-Based Abductive Natural Language Inference](https://aclanthology.org/2022.coling-1.134/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
35. [Compliance Checking with NLI: Privacy Policies vs. Regulations](https://arxiv.org/abs/2204.01845) *— 2022 · arXiv:2204.01845*
36. [ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence](https://aclanthology.org/2022.naacl-main.400/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
37. [Data Augmentation for Biomedical Factoid Question Answering](https://aclanthology.org/2022.bionlp-1.6/) *— 2022 · Proceedings of the 21st Workshop on Biomedical Language Processing*
38. [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://arxiv.org/abs/2201.05596) *— 2022 · arXiv:2201.05596*
39. [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*
40. [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) *— 2022 · arXiv:2212.09251*
41. [Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning](https://aclanthology.org/2022.coling-1.231/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
42. [E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning](https://aclanthology.org/2022.findings-acl.311/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
43. [Editing Models with Task Arithmetic](https://arxiv.org/abs/2212.04089) *— 2022 · arXiv:2212.04089*
44. [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*
45. [ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models](https://aclanthology.org/2022.naacl-main.258/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
46. [Evaluating the Knowledge Dependency of Questions](https://aclanthology.org/2022.emnlp-main.718/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
47. [Exploring The Landscape of Distributional Robustness for Question Answering Models](https://aclanthology.org/2022.findings-emnlp.441/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
48. [FQuAD2.0: French Question Answering and Learning When You Don”t Know](https://aclanthology.org/2022.lrec-1.237/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
49. [Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.research.elamur.ai/free/en/papers/xu-2022-fantastic-questions-where-find-them/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [Feeding What You Need by Understanding What You Learned](https://aclanthology.org/2022.acl-long.403/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
51. [Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT](https://aclanthology.org/2022.acl-long.330/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling](https://aclanthology.org/2022.louhi-1.19/) *— 2022 · Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)*
53. [Hyperdecoders: Instance-specific decoders for multi-task NLP](https://aclanthology.org/2022.findings-emnlp.124/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
54. [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)*
55. [JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts](https://aclanthology.org/2022.semeval-1.147/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
56. [KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering](https://aclanthology.org/2022.emnlp-main.206/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
57. [Learning Invariant Representation Improves Robustness for MRC Models](https://aclanthology.org/2022.findings-emnlp.241/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
58. [Leveraging Large Language Models for Multiple Choice Question Answering](https://arxiv.org/abs/2210.12353) *— 2022 · arXiv:2210.12353*
59. [Life after BERT: What do Other Muppets Understand about Language?](https://aclanthology.org/2022.acl-long.227/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [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)*
61. [LogiTorch: A PyTorch-based library for logical reasoning on natural language](https://aclanthology.org/2022.emnlp-demos.25/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
62. [Logic Traps in Evaluating Attribution Scores](https://aclanthology.org/2022.acl-long.407/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://aclanthology.org/2022.findings-acl.71/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
64. [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*
65. [Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering](https://aclanthology.org/2022.emnlp-main.247/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
66. [OPERA: Operation-Pivoted Discrete Reasoning over Text](https://aclanthology.org/2022.naacl-main.119/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
67. [PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation](https://aclanthology.org/2022.emnlp-industry.60/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track*
68. [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/abs/2204.02311) *— 2022 · arXiv:2204.02311*
70. [Perturbation Augmentation for Fairer NLP](https://aclanthology.org/2022.emnlp-main.646/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
71. [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*
72. [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://aclanthology.org/2022.naacl-main.313/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
73. [QAConv: Question Answering on Informative Conversations](https://aclanthology.org/2022.acl-long.370/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [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*
75. [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://aclanthology.org/2022.findings-acl.59/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
76. [RealTime QA: What”s the Answer Right Now?](https://arxiv.org/abs/2207.13332) *— 2022 · arXiv:2207.13332*
77. [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*
78. [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)*
79. [Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments](https://aclanthology.org/2022.findings-emnlp.544/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
80. [Self-critiquing models for assisting human evaluators](https://arxiv.org/abs/2206.05802) *— 2022 · arXiv:2206.05802*
81. [Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems](https://aclanthology.org/2022.eamt-1.18/) *— 2022 · Proceedings of the 23rd Annual Conference of the European Association for Machine Translation*
82. [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://aclanthology.org/2022.naacl-main.132/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
83. [To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://aclanthology.org/2022.findings-naacl.96/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
84. [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556) *— 2022 · arXiv:2203.15556*
85. [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*
86. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
87. [Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training](https://aclanthology.org/2022.findings-naacl.183/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
88. [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/abs/2201.11990) *— 2022 · arXiv:2201.11990*
89. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
90. [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/abs/2204.05832) *— 2022 · arXiv:2204.05832*
91. [What Language Model to Train if You Have One Million GPU Hours?](https://arxiv.org/abs/2210.15424) *— 2022 · arXiv:2210.15424*
92. [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)*
93. [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*
94. [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://arxiv.org/abs/2206.01861) *— 2022 · arXiv:2206.01861*
95. [`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*
96. [基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)](https://aclanthology.org/2022.ccl-1.51/) *— 2022 · Proceedings of the 21st Chinese National Conference on Computational Linguistics*
97. [A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph](https://aclanthology.org/2021.ccl-1.95/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
98. [A Robustly Optimized BERT Pre-training Approach with Post-training](https://aclanthology.org/2021.ccl-1.108/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
99. [Answering Chinese Elementary School Social Studies Multiple Choice Questions](https://aclanthology.org/2021.ijclclp-2.5/) *— 2021*
100. [Arabic Offensive Language on Twitter: Analysis and Experiments](https://aclanthology.org/2021.wanlp-1.13/) *— 2021 · Proceedings of the Sixth Arabic Natural Language Processing Workshop*

Pages: [1](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/3/) · **4** · [5](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/5/) · [6](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/) · [7](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/)