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

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/)

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1. [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*
2. [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)*
3. [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)*
4. [Large Language Models: A Survey](https://arxiv.org/abs/2402.06196) *— 2024 · arXiv:2402.06196*
5. [Large language models for education: A survey and outlook](https://arxiv.org/abs/2403.18105) *— 2024*
6. [LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding](https://arxiv.org/abs/2404.16710) *— 2024 · arXiv:2404.16710*
7. [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)*
8. [MIND: Math Informed syNthetic Dialogues for Pretraining LLMs](https://arxiv.org/abs/2410.12881) *— 2024 · arXiv:2410.12881*
9. [Make Your LLM Fully Utilize the Context](https://arxiv.org/abs/2404.16811) *— 2024 · arXiv:2404.16811*
10. [Maximize Your Data''s Potential: Enhancing LLM Accuracy with Two-Phase Pretraining](https://arxiv.org/abs/2412.15285) *— 2024 · arXiv:2412.15285*
11. [Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance](https://arxiv.org/abs/2403.09085) *— 2024 · arXiv:2403.09085*
12. [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)*
13. [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*
14. [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*
15. [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*
16. [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*
17. [Orion-14B: Open-source Multilingual Large Language Models](https://arxiv.org/abs/2401.12246) *— 2024 · arXiv:2401.12246*
18. [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)*
19. [Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models](https://arxiv.org/abs/2411.12580) *— 2024 · arXiv:2411.12580*
20. [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*
21. [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)*
22. [ShortGPT: Layers in Large Language Models are More Redundant Than You Expect](https://arxiv.org/abs/2403.03853) *— 2024 · arXiv:2403.03853*
23. [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)*
24. [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*
25. [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*
26. [The Critique of Critique](https://aclanthology.org/2024.findings-acl.538/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
27. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
28. [The Mamba in the Llama: Distilling and Accelerating Hybrid Models](https://arxiv.org/abs/2408.15237) *— 2024 · arXiv:2408.15237*
29. [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*
30. [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*
31. [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*
32. [Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning](https://arxiv.org/abs/2402.05808) *— 2024 · arXiv:2402.05808*
33. [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)*
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. [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)*
38. [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)*
39. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
40. [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*
41. [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)*
42. [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://arxiv.org/abs/2308.08747) *— 2023 · arXiv:2308.08747*
43. [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*
44. [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*
45. [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) *— 2023 · arXiv:2303.17564*
46. [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)*
47. [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*
48. [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*
49. [Cross-corpus Readability Compatibility Assessment for English Texts](https://arxiv.org/abs/2306.09704) *— 2023 · arXiv:2306.09704*
50. [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) *— 2023 · arXiv:2306.09030*
51. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
52. [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*
53. [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*
54. [Don't Make Your LLM an Evaluation Benchmark Cheater](https://arxiv.org/abs/2311.01964) *— 2023 · arXiv:2311.01964*
55. [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)*
56. [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://arxiv.org/abs/2304.08177) *— 2023 · arXiv:2304.08177*
57. [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)*
58. [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)*
59. [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*
60. [Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training](https://arxiv.org/abs/2307.14666) *— 2023 · arXiv:2307.14666*
61. [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*
62. [In-context Pretraining: Language Modeling Beyond Document Boundaries](https://arxiv.org/abs/2310.10638) *— 2023 · arXiv:2310.10638*
63. [Inspecting and Editing Knowledge Representations in Language Models](https://arxiv.org/abs/2304.00740) *— 2023 · arXiv:2304.00740*
64. [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*
65. [Knowledge Sanitization of Large Language Models](https://arxiv.org/abs/2309.11852) *— 2023 · arXiv:2309.11852*
66. [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*
67. [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) *— 2023 · arXiv:2302.13971*
68. [LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://arxiv.org/abs/2304.14402) *— 2023 · arXiv:2304.14402*
69. [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*
70. [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)*
71. [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)*
72. [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*
73. [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)*
74. [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)*
75. [On the application of Large Language Models for language teaching and assessment technology](https://arxiv.org/abs/2307.08393) *— 2023 · arXiv:2307.08393*
76. [Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression](https://arxiv.org/abs/2301.09830) *— 2023 · arXiv:2301.09830*
77. [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/abs/2311.11045) *— 2023 · arXiv:2311.11045*
78. [PaLM 2 Technical Report](https://arxiv.org/abs/2305.10403) *— 2023 · arXiv:2305.10403*
79. [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)*
80. [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)*
81. [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*
82. [Representation Engineering: A Top-Down Approach to AI Transparency](https://arxiv.org/abs/2310.01405) *— 2023 · arXiv:2310.01405*
83. [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*
84. [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*
85. [Shepherd: A Critic for Language Model Generation](https://arxiv.org/abs/2308.04592) *— 2023 · arXiv:2308.04592*
86. [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*
87. [Skywork: A More Open Bilingual Foundation Model](https://arxiv.org/abs/2310.19341) *— 2023 · arXiv:2310.19341*
88. [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*
89. [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675) *— 2023 · arXiv:2302.06675*
90. [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*
91. [The Falcon Series of Open Language Models](https://arxiv.org/abs/2311.16867) *— 2023 · arXiv:2311.16867*
92. [The Generative AI Paradox: "What It Can Create, It May Not Understand](https://arxiv.org/abs/2311.00059) *— 2023 · arXiv:2311.00059*
93. [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*
94. [UltraFeedback: Boosting Language Models with Scaled AI Feedback](https://arxiv.org/abs/2310.01377) *— 2023 · arXiv:2310.01377*
95. [Universal Self-Adaptive Prompting](https://aclanthology.org/2023.emnlp-main.461/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
96. [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)*
97. [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*
98. [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*
99. [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)*
100. [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)*

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