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

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

1. [A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation](https://aclanthology.org/2026.acl-long.1267/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference](https://aclanthology.org/2026.acl-long.437/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Audio Token Attention Is Predictable Before the Language Model Runs](https://arxiv.org/abs/2609.38878) *— 2026 · arXiv:2609.38878*
4. [CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation](https://arxiv.org/abs/2608.12842) *— 2026 · arXiv:2608.12842*
5. [Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?](https://aclanthology.org/2026.acl-long.1241/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [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)*
7. [Confidence-Aware Alignment Makes Reasoning LLMs More Reliable](https://arxiv.org/abs/2605.07353) *— 2026 · arXiv:2605.07353*
8. [DeaMoE: Efficient MoE Structure for Fast Small-Batch Decoding](https://arxiv.org/abs/2608.14385) *— 2026 · arXiv:2608.14385*
9. [Development of Serbian QA Datasets through Prompt-Based Generation and Human Validation](https://aclanthology.org/2026.lrec-1.267/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
10. [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)*
11. [Estonian Native Large Language Model Benchmark](https://aclanthology.org/2026.lrec-1.335/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
12. [From Generation to Evaluation: A Resource for Error-Categorized Question Generation from Video Transcripts](https://aclanthology.org/2026.lrec-1.170/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
13. [From Outcome to Process: Optimizing MoE Load Balancing with MCTS](https://aclanthology.org/2026.findings-acl.1440/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
14. [GMoE: Global Mixture of Experts with Logit Propagation](https://aclanthology.org/2026.acl-long.2065/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [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)*
16. [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)*
17. [Intra-Prompt Parallel Decoding for Common-Context Question Answering](https://arxiv.org/abs/2609.05707) *— 2026 · arXiv:2609.05707*
18. [Jais 2: A Family of Arabic-Centric Open Large Language Models](https://arxiv.org/abs/2608.13580) *— 2026 · arXiv:2608.13580*
19. [LLM Parameters for Math Across Languages: Shared or Separate?](https://arxiv.org/abs/2606.18453) *— 2026 · arXiv:2606.18453*
20. [Layer-wise Curriculum Learning for Efficient LLM Compression](https://arxiv.org/abs/2609.19213) *— 2026 · arXiv:2609.19213*
21. [LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation](https://arxiv.org/abs/2602.11451) *— 2026 · arXiv:2602.11451*
22. [LuxDiagRC: A Diagnostic Reading Comprehension Corpus for Luxembourgish with Linguistic and Cognitive Annotation Layers](https://aclanthology.org/2026.loreslm-1.46/) *— 2026 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)*
23. [Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs](https://arxiv.org/abs/2609.31975) *— 2026 · arXiv:2609.31975*
24. [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*
25. [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*
26. [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)*
27. [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*
28. [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*
29. [Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation](https://arxiv.org/abs/2604.22783) *— 2026 · arXiv:2604.22783*
30. [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*
31. [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)*
32. [Strong Teacher Not Needed? On Distillation in LLM Pretraining](https://arxiv.org/abs/2605.23857) *— 2026 · arXiv:2605.23857*
33. [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*
34. [Task Competence Is Not Instruction Following: Evaluating Instruction-Conflicting Behavior in Small Language Models](https://arxiv.org/abs/2607.19608) *— 2026 · arXiv:2607.19608*
35. [Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration](https://arxiv.org/abs/2608.28511) *— 2026 · arXiv:2608.28511*
36. [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)*
37. [A Simple Linear Patch Revives Layer-Pruned Large Language Models](https://arxiv.org/abs/2505.24680) *— 2025 · arXiv:2505.24680*
38. [A Survey on Automated Distractor Evaluation in Multiple-Choice Tasks](https://aclanthology.org/2025.bea-1.5/) *— 2025 · Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)*
39. [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*
40. [APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport](https://aclanthology.org/2025.emnlp-main.281/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
41. [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*
42. [ARC `Challenge' Is Not That Challenging](https://aclanthology.org/2025.findings-acl.144/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
43. [ATGen: A Framework for Active Text Generation](https://aclanthology.org/2025.acl-demo.63/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
44. [Advancing MoE Efficiency: A Collaboration-Constrained Routing ($\textttC2R$) Strategy for Better Expert Parallelism Design](https://aclanthology.org/2025.naacl-long.347/) *— 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)*
45. [Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design](https://arxiv.org/abs/2504.01337) *— 2025 · arXiv:2504.01337*
46. [Better Embeddings with Coupled Adam](https://aclanthology.org/2025.acl-long.1321/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [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)*
48. [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)*
49. [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*
50. [Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient](https://aclanthology.org/2025.acl-long.1421/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
51. [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)*
52. [CrossQG: Improving Difficulty-Controllable Question Generation through Consistency Enhancement](https://aclanthology.org/2025.findings-emnlp.151/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
53. [DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts](https://aclanthology.org/2025.acl-long.951/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [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*
55. [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)*
56. [Development of Numerical Error Detection Tasks to Analyze the Numerical Capabilities of Language Models](https://aclanthology.org/2025.coling-main.666/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
57. [Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination](https://aclanthology.org/2025.findings-acl.60/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
58. [Dream 7B: Diffusion Large Language Models](https://arxiv.org/abs/2508.15487) *— 2025 · arXiv:2508.15487*
59. [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)*
60. [Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration](https://aclanthology.org/2025.acl-long.466/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning](https://aclanthology.org/2025.findings-naacl.465/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
62. [Enhancing Language Model Hypernetworks with Restart: A Study on Optimization](https://aclanthology.org/2025.naacl-long.397/) *— 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)*
63. [Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs](https://arxiv.org/abs/2503.05139) *— 2025 · arXiv:2503.05139*
64. [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)*
65. [From Chat Logs to Collective Insights: Aggregative Question Answering](https://aclanthology.org/2025.emnlp-main.1667/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
66. [From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning](https://aclanthology.org/2025.findings-emnlp.181/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
67. [Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data](https://arxiv.org/abs/2510.03264) *— 2025 · arXiv:2510.03264*
68. [GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression](https://aclanthology.org/2025.emnlp-main.1338/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
69. [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)*
70. [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*
71. [GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models](https://aclanthology.org/2025.acl-long.255/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
72. [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*
73. [Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers](https://arxiv.org/abs/2502.01770) *— 2025 · arXiv:2502.01770*
74. [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*
75. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
76. [Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance](https://aclanthology.org/2025.ijcnlp-long.189/) *— 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*
77. [Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks](https://aclanthology.org/2025.coling-main.590/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
78. [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://arxiv.org/abs/2508.18265) *— 2025 · arXiv:2508.18265*
79. [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://arxiv.org/abs/2504.10479) *— 2025 · arXiv:2504.10479*
80. [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*
81. [LESA: Learnable LLM Layer Scaling-Up](https://aclanthology.org/2025.acl-long.1095/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
82. [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*
83. [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*
84. [Linear Steerability in Language Models: When It Emerges and How It Evolves](https://aclanthology.org/2025.findings-emnlp.969/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
85. [Linguistic Entity Masking to Improve Cross-Lingual Representation of Multilingual Language Models for Low-Resource Languages](https://arxiv.org/abs/2501.05700) *— 2025 · arXiv:2501.05700*
86. [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*
87. [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)*
88. [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://aclanthology.org/2025.findings-emnlp.655/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
89. [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)*
90. [Maximum Score Routing For Mixture-of-Experts](https://aclanthology.org/2025.findings-acl.653/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
91. [Measuring Social Biases in Masked Language Models by Proxy of Prediction Quality](https://aclanthology.org/2025.acl-long.68/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
92. [MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection](https://aclanthology.org/2025.coling-main.649/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
93. [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)*
94. [MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining](https://arxiv.org/abs/2505.07608) *— 2025 · arXiv:2505.07608*
95. [Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation](https://aclanthology.org/2025.findings-emnlp.578/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
96. [Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets](https://arxiv.org/abs/2512.18834) *— 2025 · arXiv:2512.18834*
97. [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*
98. [Morables: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables](https://aclanthology.org/2025.emnlp-main.1411/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
99. [MossNet: Mixture of State-Space Experts is a Multi-Head Attention](https://aclanthology.org/2025.ijcnlp-long.26/) *— 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*
100. [Multilingual Iterative Model Pruning: What Matters?](https://aclanthology.org/2025.ijcnlp-long.32/) *— 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*

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