---
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/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [RACE: Large-scale ReAding Comprehension Dataset…](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/) / cited by

# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 3

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

1. [Mitigating Bias for Question Answering Models by Tracking Bias Influence](https://aclanthology.org/2024.naacl-long.257/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
2. [Mitigating the Alignment Tax of RLHF](https://aclanthology.org/2024.emnlp-main.35/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
3. [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://arxiv.org/abs/2402.14905) *— 2024 · arXiv:2402.14905*
4. [MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension](https://aclanthology.org/2024.findings-emnlp.216/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
5. [More Insightful Feedback for Tutoring: Enhancing Generation Mechanisms and Automatic Evaluation](https://aclanthology.org/2024.emnlp-main.605/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
6. [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*
7. [Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination](https://aclanthology.org/2024.emnlp-main.740/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
8. [On the Universal Truthfulness Hyperplane Inside LLMs](https://aclanthology.org/2024.emnlp-main.1012/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
9. [On the Way to Lossless Compression of Language Transformers: Exploring Cross-Domain Properties of Quantization](https://aclanthology.org/2024.lrec-main.1089/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
10. [Orion-14B: Open-source Multilingual Large Language Models](https://arxiv.org/abs/2401.12246) *— 2024 · arXiv:2401.12246*
11. [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://aclanthology.org/2024.acl-short.55/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
12. [PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering](https://aclanthology.org/2024.acl-long.724/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models](https://arxiv.org/abs/2411.12580) *— 2024 · arXiv:2411.12580*
14. [Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging](https://aclanthology.org/2024.emnlp-main.987/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
15. [Qsnail: A Questionnaire Dataset for Sequential Question Generation](https://aclanthology.org/2024.lrec-main.1174/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
16. [RegMix: Data Mixture as Regression for Language Model Pre-training](https://arxiv.org/abs/2407.01492) *— 2024 · arXiv:2407.01492*
17. [Revisiting Catastrophic Forgetting in Large Language Model Tuning](https://aclanthology.org/2024.findings-emnlp.249/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
18. [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*
19. [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)*
20. [Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models](https://aclanthology.org/2024.findings-naacl.162/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
21. [ShortGPT: Layers in Large Language Models are More Redundant Than You Expect](https://arxiv.org/abs/2403.03853) *— 2024 · arXiv:2403.03853*
22. [TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya](https://aclanthology.org/2024.lrec-main.1404/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
23. [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*
24. [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://aclanthology.org/2024.findings-acl.275/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
25. [The Critique of Critique](https://aclanthology.org/2024.findings-acl.538/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
26. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
27. [The Mamba in the Llama: Distilling and Accelerating Hybrid Models](https://arxiv.org/abs/2408.15237) *— 2024 · arXiv:2408.15237*
28. [To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models](https://aclanthology.org/2024.findings-emnlp.82/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
29. [Tokenization Is More Than Compression](https://aclanthology.org/2024.emnlp-main.40/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
30. [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://aclanthology.org/2024.findings-naacl.247/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
31. [Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning](https://arxiv.org/abs/2402.05808) *— 2024 · arXiv:2402.05808*
32. [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)*
33. [Unleashing the Potential of Large Language Models through Spectral Modulation](https://aclanthology.org/2024.findings-emnlp.224/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
34. [Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding](https://aclanthology.org/2024.findings-acl.47/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
35. [Using LLMs to simulate students' responses to exam questions](https://aclanthology.org/2024.findings-emnlp.663/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
36. [Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness](https://aclanthology.org/2024.naacl-long.310/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
37. [eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization](https://arxiv.org/abs/2405.13938) *— 2024 · arXiv:2405.13938*
38. [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)*
39. [項目反応理論を用いた難易度調整可能な多肢選択式読解問題自動生成](https://www.anlp.jp/proceedings/annual_meeting/2024/#P4-21) *— 2024 · 言語処理学会 第30回年次大会 発表論文集 (NLP2024)*
40. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
41. [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://aclanthology.org/2023.findings-acl.29/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
42. [AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://aclanthology.org/2023.acl-long.634/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://arxiv.org/abs/2308.08747) *— 2023 · arXiv:2308.08747*
44. [Assessing Distractors in Multiple-Choice Tests](https://aclanthology.org/2023.eval4nlp-1.2/) *— 2023 · Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems*
45. [Attack Prompt Generation for Red Teaming and Defending Large Language Models](https://aclanthology.org/2023.findings-emnlp.143/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
46. [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) *— 2023 · arXiv:2303.17564*
47. [Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models](https://aclanthology.org/2023.findings-emnlp.738/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
48. [Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models](https://aclanthology.org/2023.acl-long.738/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://arxiv.org/abs/2308.05502) *— 2023 · arXiv:2308.05502 · Artif Intell Law, Springer Nature. November 2023*
50. [CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model](https://aclanthology.org/2023.emnlp-main.597/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
51. [Catwalk: A Unified Language Model Evaluation Framework for Many Datasets](https://arxiv.org/abs/2312.10253) *— 2023 · arXiv:2312.10253*
52. [ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence](https://aclanthology.org/2023.ranlp-1.11/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
53. [Cross-corpus Readability Compatibility Assessment for English Texts](https://arxiv.org/abs/2306.09704) *— 2023 · arXiv:2306.09704*
54. [Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification](https://aclanthology.org/2023.findings-acl.328/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
55. [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) *— 2023 · arXiv:2306.09030*
56. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
57. [Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers](https://aclanthology.org/2023.findings-acl.480/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
58. [Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation](https://aclanthology.org/2023.findings-acl.790/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
59. [Domain Incremental Lifelong Learning in an Open World](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
60. [Don't Make Your LLM an Evaluation Benchmark Cheater](https://arxiv.org/abs/2311.01964) *— 2023 · arXiv:2311.01964*
61. [Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering](https://aclanthology.org/2023.acl-long.785/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://arxiv.org/abs/2304.08177) *— 2023 · arXiv:2304.08177*
63. [Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://aclanthology.org/2023.findings-emnlp.866/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
64. [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)*
65. [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*
66. [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)*
67. [Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision](https://aclanthology.org/2023.emnlp-main.1001/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
68. [IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning](https://aclanthology.org/2023.findings-acl.513/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
69. [Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training](https://arxiv.org/abs/2307.14666) *— 2023 · arXiv:2307.14666*
70. [In-Context Demonstration Selection with Cross Entropy Difference](https://aclanthology.org/2023.findings-emnlp.81/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
71. [In-context Pretraining: Language Modeling Beyond Document Boundaries](https://arxiv.org/abs/2310.10638) *— 2023 · arXiv:2310.10638*
72. [Inspecting and Editing Knowledge Representations in Language Models](https://arxiv.org/abs/2304.00740) *— 2023 · arXiv:2304.00740*
73. [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*
74. [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/abs/2308.16149) *— 2023 · arXiv:2308.16149*
75. [Knowledge Sanitization of Large Language Models](https://arxiv.org/abs/2309.11852) *— 2023 · arXiv:2309.11852*
76. [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*
77. [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) *— 2023 · arXiv:2302.13971*
78. [LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://arxiv.org/abs/2304.14402) *— 2023 · arXiv:2304.14402*
79. [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*
80. [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)*
81. [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)*
82. [MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.343/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
83. [MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization](https://aclanthology.org/2023.ijcnlp-main.4/) *— 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)*
84. [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*
85. [Machine Reading Comprehension using Case-based Reasoning](https://aclanthology.org/2023.findings-emnlp.564/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
86. [Measure Children's Mindreading Ability with Machine Reading](https://aclanthology.org/2023.findings-emnlp.561/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
87. [Multilingual Conceptual Coverage in Text-to-Image Models](https://aclanthology.org/2023.acl-long.266/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Natural Response Generation for Chinese Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.739/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
89. [On Degrees of Freedom in Defining and Testing Natural Language Understanding](https://aclanthology.org/2023.findings-acl.861/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
90. [On the application of Large Language Models for language teaching and assessment technology](https://arxiv.org/abs/2307.08393) *— 2023 · arXiv:2307.08393*
91. [Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression](https://arxiv.org/abs/2301.09830) *— 2023 · arXiv:2301.09830*
92. [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/abs/2311.11045) *— 2023 · arXiv:2311.11045*
93. [PaLM 2 Technical Report](https://arxiv.org/abs/2305.10403) *— 2023 · arXiv:2305.10403*
94. [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)*
95. [Post Turing: Mapping the landscape of LLM Evaluation](https://aclanthology.org/2023.gem-1.31/) *— 2023 · Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
96. [Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA](https://aclanthology.org/2023.acl-long.55/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [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*
98. [Representation Engineering: A Top-Down Approach to AI Transparency](https://arxiv.org/abs/2310.01405) *— 2023 · arXiv:2310.01405*
99. [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://aclanthology.org/2023.emnlp-main.557/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
100. [Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study](https://aclanthology.org/2023.emnlp-main.482/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*

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