---
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/2/"
---
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# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 2

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

1. [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://aclanthology.org/2025.tacl-1.25/) *— 2025*
2. [Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://aclanthology.org/2025.acl-long.123/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models](https://arxiv.org/abs/2506.07731) *— 2025 · arXiv:2506.07731*
4. [Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts](https://aclanthology.org/2025.arabicnlp-main.25/) *— 2025 · Proceedings of The Third Arabic Natural Language Processing Conference*
5. [Open-DeBias: Toward Mitigating Open-Set Bias in Language Models](https://aclanthology.org/2025.findings-emnlp.1364/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
6. [Option Symbol Matters: Investigating and Mitigating Multiple-Choice Option Symbol Bias of Large Language Models](https://aclanthology.org/2025.naacl-long.95/) *— 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)*
7. [PATCH: Learnable Tile-level Hybrid Sparsity for LLMs](https://arxiv.org/abs/2509.23410) *— 2025 · arXiv:2509.23410*
8. [PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models](https://aclanthology.org/2025.acl-long.225/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity](https://arxiv.org/abs/2505.21411) *— 2025 · arXiv:2505.21411*
10. [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://arxiv.org/abs/2504.07866) *— 2025 · arXiv:2504.07866*
11. [PersianMCQ-Instruct: A Comprehensive Resource for Generating Multiple-Choice Questions in Persian](https://aclanthology.org/2025.loreslm-1.27/) *— 2025 · Proceedings of the First Workshop on Language Models for Low-Resource Languages*
12. [Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models](https://aclanthology.org/2025.findings-emnlp.962/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
13. [Probing and Boosting Large Language Models Capabilities via Attention Heads](https://aclanthology.org/2025.emnlp-main.1450/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
14. [Prompt Compression for Large Language Models: A Survey](https://aclanthology.org/2025.naacl-long.368/) *— 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)*
15. [RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging](https://aclanthology.org/2025.emnlp-main.829/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
16. [Recipes for Pre-training LLMs with MXFP8](https://arxiv.org/abs/2506.08027) *— 2025 · arXiv:2506.08027*
17. [RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?](https://arxiv.org/abs/2501.11284) *— 2025 · arXiv:2501.11284*
18. [Rubrik”s Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset](https://aclanthology.org/2025.acl-long.1160/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View](https://aclanthology.org/2025.acl-long.852/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [SMoE: An Algorithm-System Co-Design for Pushing MoE to the Edge via Expert Substitution](https://arxiv.org/abs/2508.18983) *— 2025 · arXiv:2508.18983*
21. [SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model](https://aclanthology.org/2025.naacl-long.230/) *— 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)*
22. [Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion](https://aclanthology.org/2025.acl-long.100/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Self-Adjust Softmax](https://aclanthology.org/2025.emnlp-main.397/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
24. [ShortGPT: Layers in Large Language Models are More Redundant Than You Expect](https://aclanthology.org/2025.findings-acl.1035/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
25. [Stochastic activations](https://arxiv.org/abs/2509.22358) *— 2025 · arXiv:2509.22358*
26. [TEEMIL : Towards Educational MCQ Difficulty Estimation in Indic Languages](https://aclanthology.org/2025.coling-main.142/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
27. [TORSO: Template-Oriented Reasoning Towards General Tasks](https://aclanthology.org/2025.emnlp-main.851/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
28. [The Gaps between Fine Tuning and In-context Learning in Bias Evaluation and Debiasing](https://aclanthology.org/2025.coling-main.187/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
29. [The Threat of PROMPTS in Large Language Models: A System and User Prompt Perspective](https://aclanthology.org/2025.findings-acl.675/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
30. [The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models](https://aclanthology.org/2025.acl-long.590/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [Training Language Models to Critique With Multi-agent Feedback](https://aclanthology.org/2025.findings-emnlp.78/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
32. [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*
33. [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)*
34. [Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models](https://aclanthology.org/2025.coling-main.208/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
35. [Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models](https://aclanthology.org/2025.acl-long.1441/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://aclanthology.org/2025.acl-long.169/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [X-EcoMLA: Upcycling Pre-Trained Attention into MLA for Efficient and Extreme KV Compression](https://arxiv.org/abs/2503.11132) *— 2025 · arXiv:2503.11132*
38. [YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model](https://aclanthology.org/2025.acl-long.268/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [\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*
40. [État de l”art : évaluation, détection et mitigation des hallucinations des LLMs](https://aclanthology.org/2025.jeptalnrecital-recital.7/) *— 2025 · Actes des 18e Rencontres Jeunes Chercheurs en RI (RJCRI) et 27ème Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL)*
41. [難易度調整可能な多枝選択式読解問題自動生成手法とDirect Preference Optimizationによる難易度調整精度の向上](https://www.anlp.jp/proceedings/annual_meeting/2025/#C6-3) *— 2025 · 言語処理学会 第31回年次大会 発表論文集 (NLP2025)*
42. [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://aclanthology.org/2024.emnlp-main.764/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
43. [A study of Vietnamese readability assessing through semantic and statistical features](https://aclanthology.org/2024.paclic-1.7/) *— 2024 · Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation*
44. [AMPO: Automatic Multi-Branched Prompt Optimization](https://aclanthology.org/2024.emnlp-main.1130/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
45. [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://arxiv.org/abs/2411.00640) *— 2024 · arXiv:2411.00640*
46. [An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887) *— 2024 · arXiv:2406.07887*
47. [An Information-Theoretic Approach to Analyze NLP Classification Tasks](https://aclanthology.org/2024.acl-long.32/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Analysing The Impact of Sequence Composition on Language Model Pre-Training](https://aclanthology.org/2024.acl-long.427/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [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*
50. [Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models](https://aclanthology.org/2024.readi-1.3/) *— 2024 · Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024*
51. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
52. [Bilingual Adaptation of Monolingual Foundation Models](https://arxiv.org/abs/2407.12869) *— 2024 · arXiv:2407.12869*
53. [Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension](https://aclanthology.org/2024.bionlp-1.20/) *— 2024 · Proceedings of the 23rd Workshop on Biomedical Natural Language Processing*
54. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
55. [Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method](https://aclanthology.org/2024.findings-emnlp.152/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
56. [DDK: Distilling Domain Knowledge for Efficient Large Language Models](https://arxiv.org/abs/2407.16154) *— 2024 · arXiv:2407.16154*
57. [Data, Data Everywhere: A Guide for Pretraining Dataset Construction](https://aclanthology.org/2024.emnlp-main.596/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
58. [Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning](https://aclanthology.org/2024.findings-acl.559/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
59. [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://arxiv.org/abs/2401.02954) *— 2024 · arXiv:2401.02954*
60. [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) *— 2024 · arXiv:2405.04434*
61. [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) *— 2024 · arXiv:2412.19437*
62. [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://aclanthology.org/2024.acl-long.70/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [Demystifying Instruction Mixing for Fine-tuning Large Language Models](https://aclanthology.org/2024.acl-srw.15/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
64. [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)*
65. [Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction](https://aclanthology.org/2024.findings-acl.175/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
66. [Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models](https://aclanthology.org/2024.lrec-main.452/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
67. [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*
68. [Do Multilingual Large Language Models Mitigate Stereotype Bias?](https://aclanthology.org/2024.c3nlp-1.6/) *— 2024 · Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP*
69. [DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling](https://aclanthology.org/2024.naacl-long.182/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
70. [E^2-LLM: Efficient and Extreme Length Extension of Large Language Models](https://arxiv.org/abs/2401.06951) *— 2024 · arXiv:2401.06951*
71. [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://arxiv.org/abs/2412.05271) *— 2024 · arXiv:2412.05271*
72. [Exploring Self-supervised Logic-enhanced Training for Large Language Models](https://aclanthology.org/2024.naacl-long.53/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
73. [Eye-Tracking Features Masking Transformer Attention in Question-Answering Tasks](https://aclanthology.org/2024.lrec-main.619/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
74. [GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond](https://aclanthology.org/2024.findings-naacl.87/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
75. [GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning](https://arxiv.org/abs/2407.02147) *— 2024 · arXiv:2407.02147*
76. [Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding](https://aclanthology.org/2024.findings-acl.313/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
77. [Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs](https://arxiv.org/abs/2405.02774) *— 2024 · arXiv:2405.02774*
78. [Head-wise Shareable Attention for Large Language Models](https://aclanthology.org/2024.findings-emnlp.144/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
79. [HiRE: High Recall Approximate Top-$k$ Estimation for Efficient LLM Inference](https://arxiv.org/abs/2402.09360) *— 2024 · arXiv:2402.09360*
80. [Improving Bengali and Hindi Large Language Models](https://aclanthology.org/2024.lrec-main.764/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
81. [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*
82. [InternLM2 Technical Report](https://arxiv.org/abs/2403.17297) *— 2024 · arXiv:2403.17297*
83. [Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning](https://aclanthology.org/2024.emnlp-main.1004/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
84. [KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing](https://arxiv.org/abs/2410.18517) *— 2024 · arXiv:2410.18517*
85. [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*
86. [LFED: A Literary Fiction Evaluation Dataset for Large Language Models](https://aclanthology.org/2024.lrec-main.915/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
87. [LLMBox: A Comprehensive Library for Large Language Models](https://aclanthology.org/2024.acl-demos.37/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
88. [LaCo: Large Language Model Pruning via Layer Collapse](https://aclanthology.org/2024.findings-emnlp.372/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
89. [LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://aclanthology.org/2024.eacl-long.57/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
90. [Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks](https://aclanthology.org/2024.acl-long.621/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
91. [Large Language Models: A Survey](https://arxiv.org/abs/2402.06196) *— 2024 · arXiv:2402.06196*
92. [Large language models for education: A survey and outlook](https://arxiv.org/abs/2403.18105) *— 2024*
93. [LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding](https://arxiv.org/abs/2404.16710) *— 2024 · arXiv:2404.16710*
94. [LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://aclanthology.org/2024.acl-long.106/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning](https://aclanthology.org/2024.findings-emnlp.858/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
96. [MIND: Math Informed syNthetic Dialogues for Pretraining LLMs](https://arxiv.org/abs/2410.12881) *— 2024 · arXiv:2410.12881*
97. [Make Your LLM Fully Utilize the Context](https://arxiv.org/abs/2404.16811) *— 2024 · arXiv:2404.16811*
98. [Maximize Your Data”s Potential: Enhancing LLM Accuracy with Two-Phase Pretraining](https://arxiv.org/abs/2412.15285) *— 2024 · arXiv:2412.15285*
99. [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)*
100. [Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance](https://arxiv.org/abs/2403.09085) *— 2024 · arXiv:2403.09085*

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