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

617 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/lai-2017-race-large-scale-reading-comprehension/)

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

1. [Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models](https://aclanthology.org/2026.acl-long.226/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://aclanthology.org/2026.acl-short.27/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
3. [InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling](https://aclanthology.org/2026.acl-long.383/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts](https://aclanthology.org/2026.eacl-short.33/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
5. [Jais 2: A Family of Arabic-Centric Open Large Language Models](https://arxiv.org/abs/2608.13580) *— 2026 · arXiv:2608.13580*
6. [LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation](https://arxiv.org/abs/2602.11451) *— 2026 · arXiv:2602.11451*
7. [ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings](https://aclanthology.org/2026.findings-acl.174/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://arxiv.org/abs/2604.12374) *— 2026 · arXiv:2604.12374*
9. [Offline Preference Optimization via Maximum Marginal Likelihood Estimation](https://aclanthology.org/2026.eacl-long.318/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [One Token Is Enough: Improving Diffusion Language Models with a Sink Token](https://aclanthology.org/2026.findings-acl.323/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [PHEB: An European Portuguese High School-Level LLM Benchmark](https://aclanthology.org/2026.lrec-1.367/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
12. [Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers](https://aclanthology.org/2026.findings-eacl.261/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
13. [RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents](https://aclanthology.org/2026.acl-long.558/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Suppressing Final Layer Hidden State Jumps in Transformer Pretraining](https://aclanthology.org/2026.findings-eacl.64/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
15. [500xCompressor: Generalized Prompt Compression for Large Language Models](https://aclanthology.org/2025.acl-long.1219/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [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*
17. [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*
18. [Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design](https://arxiv.org/abs/2504.01337) *— 2025 · arXiv:2504.01337*
19. [Beyond Text Compression: Evaluating Tokenizers Across Scales](https://aclanthology.org/2025.acl-long.1546/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [BlockPruner: Fine-grained Pruning for Large Language Models](https://aclanthology.org/2025.findings-acl.262/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
21. [Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement](https://aclanthology.org/2025.acl-long.1091/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation](https://aclanthology.org/2025.findings-acl.611/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
23. [Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation](https://aclanthology.org/2025.acl-long.407/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [Dream 7B: Diffusion Large Language Models](https://arxiv.org/abs/2508.15487) *— 2025 · arXiv:2508.15487*
25. [Efficient Domain Continual pretraining by Mitigating the Stability Gap](https://aclanthology.org/2025.acl-long.1578/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs](https://arxiv.org/abs/2503.05139) *— 2025 · arXiv:2503.05139*
27. [Flexora: Flexible Low-Rank Adaptation for Large Language Models](https://aclanthology.org/2025.acl-long.713/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data](https://arxiv.org/abs/2510.03264) *— 2025 · arXiv:2510.03264*
29. [Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction](https://aclanthology.org/2025.acl-long.1154/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models](https://aclanthology.org/2025.findings-acl.405/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
31. [ICLEval: Evaluating In-Context Learning Ability of Large Language Models](https://aclanthology.org/2025.coling-main.693/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
32. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
33. [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://arxiv.org/abs/2508.18265) *— 2025 · arXiv:2508.18265*
34. [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://arxiv.org/abs/2504.10479) *— 2025 · arXiv:2504.10479*
35. [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*
36. [Large Language Models Still Exhibit Bias in Long Text](https://aclanthology.org/2025.findings-acl.1341/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
37. [Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs](https://aclanthology.org/2025.findings-emnlp.372/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
38. [Math Neurosurgery: Isolating Language Models'' Math Reasoning Abilities Using Only Forward Passes](https://aclanthology.org/2025.acl-long.1209/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://aclanthology.org/2025.acl-long.533/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
40. [MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining](https://arxiv.org/abs/2505.07608) *— 2025 · arXiv:2505.07608*
41. [MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning](https://aclanthology.org/2025.coling-main.111/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
42. [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://aclanthology.org/2025.tacl-1.25/) *— 2025*
43. [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)*
44. [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*
45. [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)*
46. [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)*
47. [Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity](https://arxiv.org/abs/2505.21411) *— 2025 · arXiv:2505.21411*
48. [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://arxiv.org/abs/2504.07866) *— 2025 · arXiv:2504.07866*
49. [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*
50. [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*
51. [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)*
52. [Recipes for Pre-training LLMs with MXFP8](https://arxiv.org/abs/2506.08027) *— 2025 · arXiv:2506.08027*
53. [RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?](https://arxiv.org/abs/2501.11284) *— 2025 · arXiv:2501.11284*
54. [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)*
55. [Self-Adjust Softmax](https://aclanthology.org/2025.emnlp-main.397/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
56. [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*
57. [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*
58. [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*
59. [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*
60. [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)*
61. [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)*
62. [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)*
63. [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)*
64. [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*
65. [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*
66. [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://arxiv.org/abs/2411.00640) *— 2024 · arXiv:2411.00640*
67. [An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887) *— 2024 · arXiv:2406.07887*
68. [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)*
69. [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*
70. [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*
71. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
72. [Bilingual Adaptation of Monolingual Foundation Models](https://arxiv.org/abs/2407.12869) *— 2024 · arXiv:2407.12869*
73. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
74. [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*
75. [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*
76. [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://arxiv.org/abs/2401.02954) *— 2024 · arXiv:2401.02954*
77. [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) *— 2024 · arXiv:2405.04434*
78. [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) *— 2024 · arXiv:2412.19437*
79. [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)*
80. [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)*
81. [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)*
82. [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*
83. [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)*
84. [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*
85. [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*
86. [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*
87. [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)*
88. [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)*
89. [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*
90. [GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning](https://arxiv.org/abs/2407.02147) *— 2024 · arXiv:2407.02147*
91. [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*
92. [Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs](https://arxiv.org/abs/2405.02774) *— 2024 · arXiv:2405.02774*
93. [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*
94. [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*
95. [InternLM2 Technical Report](https://arxiv.org/abs/2403.17297) *— 2024 · arXiv:2403.17297*
96. [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*
97. [KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing](https://arxiv.org/abs/2410.18517) *— 2024 · arXiv:2410.18517*
98. [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*
99. [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)*
100. [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)*

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