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home / docs / papers / cited-by / Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 2

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

Pages: 1 · 2 · 3 · 4 · 5 · 6 · 7

  1. LaCo: Large Language Model Pruning via Layer Collapse — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  2. LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions — 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 — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  4. Large Language Models: A Survey — 2024 · arXiv:2402.06196
  5. Large language models for education: A survey and outlook — 2024
  6. LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding — 2024 · arXiv:2404.16710
  7. LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin — 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 — 2024 · arXiv:2410.12881
  9. Make Your LLM Fully Utilize the Context — 2024 · arXiv:2404.16811
  10. Maximize Your Data''s Potential: Enhancing LLM Accuracy with Two-Phase Pretraining — 2024 · arXiv:2412.15285
  11. Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance — 2024 · arXiv:2403.09085
  12. Mitigating Bias for Question Answering Models by Tracking Bias Influence — 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 — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  14. MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension — 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? — 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP
  16. Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  17. Orion-14B: Open-source Multilingual Large Language Models — 2024 · arXiv:2401.12246
  18. Paraphrasing in Affirmative Terms Improves Negation Understanding — 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 — 2024 · arXiv:2411.12580
  20. Revisiting Catastrophic Forgetting in Large Language Model Tuning — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  21. SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning — 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 — 2024 · arXiv:2403.03853
  23. TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya — 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 — 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 — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  26. The Critique of Critique — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  27. The Llama 3 Herd of Models — 2024 · arXiv:2407.21783
  28. The Mamba in the Llama: Distilling and Accelerating Hybrid Models — 2024 · arXiv:2408.15237
  29. To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  30. Tokenization Is More Than Compression — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  31. Tokenizer Choice For LLM Training: Negligible or Crucial? — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  32. Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning — 2024 · arXiv:2402.05808
  33. UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt — 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 — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  35. Using LLMs to simulate students' responses to exam questions — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  36. Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness — 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 — 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 — 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 — 2023 · arXiv:2307.06435
  40. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  41. AlignScore: Evaluating Factual Consistency with A Unified Alignment Function — 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 — 2023 · arXiv:2308.08747
  43. Assessing Distractors in Multiple-Choice Tests — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  45. BloombergGPT: A Large Language Model for Finance — 2023 · arXiv:2303.17564
  46. Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models — 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 — 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 — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  49. Cross-corpus Readability Compatibility Assessment for English Texts — 2023 · arXiv:2306.09704
  50. DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning — 2023 · arXiv:2306.09030
  51. DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI — 2023 · arXiv:2307.10172
  52. Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  53. Domain Incremental Lifelong Learning in an Open World — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  54. Don't Make Your LLM an Evaluation Benchmark Cheater — 2023 · arXiv:2311.01964
  55. Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering — 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 — 2023 · arXiv:2304.08177
  57. Few-shot Adaptation Works with UnpredicTable Data — 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 — 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 — 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 — 2023 · arXiv:2307.14666
  61. In-Context Demonstration Selection with Cross Entropy Difference — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  62. In-context Pretraining: Language Modeling Beyond Document Boundaries — 2023 · arXiv:2310.10638
  63. Inspecting and Editing Knowledge Representations in Language Models — 2023 · arXiv:2304.00740
  64. Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models — 2023 · arXiv:2308.16149
  65. Knowledge Sanitization of Large Language Models — 2023 · arXiv:2309.11852
  66. KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  67. LLaMA: Open and Efficient Foundation Language Models — 2023 · arXiv:2302.13971
  68. LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions — 2023 · arXiv:2304.14402
  69. Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU — 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? — 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 — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  73. MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization — 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 — 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 — 2023 · arXiv:2307.08393
  76. Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression — 2023 · arXiv:2301.09830
  77. Orca 2: Teaching Small Language Models How to Reason — 2023 · arXiv:2311.11045
  78. PaLM 2 Technical Report — 2023 · arXiv:2305.10403
  79. Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer — 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 — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  82. Representation Engineering: A Top-Down Approach to AI Transparency — 2023 · arXiv:2310.01405
  83. SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models — 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 — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  85. Shepherd: A Critic for Language Model Generation — 2023 · arXiv:2308.04592
  86. SkillQG: Learning to Generate Question for Reading Comprehension Assessment — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  87. Skywork: A More Open Bilingual Foundation Model — 2023 · arXiv:2310.19341
  88. Specialist or Generalist? Instruction Tuning for Specific NLP Tasks — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  89. Symbolic Discovery of Optimization Algorithms — 2023 · arXiv:2302.06675
  90. The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants — 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 — 2023 · arXiv:2311.16867
  92. The Generative AI Paradox: "What It Can Create, It May Not Understand — 2023 · arXiv:2311.00059
  93. Tunable Soft Prompts are Messengers in Federated Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  94. UltraFeedback: Boosting Language Models with Scaled AI Feedback — 2023 · arXiv:2310.01377
  95. Universal Self-Adaptive Prompting — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  96. Using contradictions improves question answering systems — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  98. Weakly-Supervised Questions for Zero-Shot Relation Extraction — 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? — 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 — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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