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home / docs / papers / cited-by / Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 2

Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 2

600 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

  1. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism — 2024 · arXiv:2401.02954
  2. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model — 2024 · arXiv:2405.04434
  3. DeepSeek-V3 Technical Report — 2024 · arXiv:2412.19437
  4. DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  5. Divide-or-Conquer? Which Part Should You Distill Your LLM? — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  6. DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  7. Downstream Trade-offs of a Family of Text Watermarks — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  8. Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision — 2024 · arXiv:2403.09472
  9. EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
  10. Ethos: Rectifying Language Models in Orthogonal Parameter Space — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  11. Eureka: Evaluating and Understanding Large Foundation Models — 2024 · arXiv:2409.10566
  12. FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  13. Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  14. From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline — 2024 · arXiv:2406.11939
  15. Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap — 2024 · arXiv:2402.19450
  16. GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  17. Gated Delta Networks: Improving Mamba2 with Delta Rule — 2024 · arXiv:2412.06464
  18. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context — 2024 · arXiv:2403.05530
  19. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent — 2024 · arXiv:2411.02265
  20. Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  21. Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  22. Improving Language Model Reasoning with Self-motivated Learning — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  23. In-Context Principle Learning from Mistakes — 2024 · arXiv:2402.05403
  24. JDocQA: Japanese Document Question Answering Dataset for Generative Language Models — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  25. Just read twice: closing the recall gap for recurrent language models — 2024 · arXiv:2407.05483
  26. Knowledge Fusion of Large Language Models — 2024 · arXiv:2401.10491
  27. LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks — 2024 · arXiv:2406.18403
  28. Large Language Models for Scientific Information Extraction: An Empirical Study for Virology — 2024 · Findings of the Association for Computational Linguistics: EACL 2024
  29. Lessons from the Trenches on Reproducible Evaluation of Language Models — 2024 · arXiv:2405.14782
  30. LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  31. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  32. LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks — 2024 · arXiv:2412.15204
  33. MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  34. Measuring Retrieval Complexity in Question Answering Systems — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  35. Mitigating the Alignment Tax of RLHF — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  36. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — 2024 · arXiv:2406.06565
  37. Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once? — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  38. 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
  39. NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  40. Number Cookbook: Number Understanding of Language Models and How to Improve It — 2024 · arXiv:2411.03766
  41. ODIN: Disentangled Reward Mitigates Hacking in RLHF — 2024 · arXiv:2402.07319
  42. PEDANTS: Cheap but Effective and Interpretable Answer Equivalence — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  43. Paraphrasing in Affirmative Terms Improves Negation Understanding — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
  44. Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models — 2024 · arXiv:2411.12580
  45. Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  46. Rationale-Aware Answer Verification by Pairwise Self-Evaluation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  47. Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus — 2024 · arXiv:2403.11793
  48. Revisiting Knowledge Distillation for Autoregressive Language Models — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  49. SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety — 2024 · arXiv:2404.05399
  50. SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  51. Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers? — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
  52. SocREval: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  53. Synthetic Multimodal Question Generation — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  54. The Llama 3 Herd of Models — 2024 · arXiv:2407.21783
  55. TinyLlama: An Open-Source Small Language Model — 2024 · arXiv:2401.02385
  56. Towards Faithful Model Explanation in NLP: A Survey — 2024 · Computational Linguistics
  57. Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  58. Tulu 3: Pushing Frontiers in Open Language Model Post-Training — 2024 · arXiv:2411.15124
  59. Unified Active Retrieval for Retrieval Augmented Generation — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  60. 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)
  61. mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  62. A Comprehensive Evaluation of Tool-Assisted Generation Strategies — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  63. A Comprehensive Overview of Large Language Models — 2023 · arXiv:2307.06435
  64. A Survey of Deep Learning for Mathematical Reasoning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  65. A Survey of Large Language Models — 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)
  66. AlpaGasus: Training A Better Alpaca with Fewer Data — 2023 · arXiv:2307.08701
  67. Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  68. Arithmetic-Based Pretraining Improving Numeracy of Pretrained Language Models — 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
  69. Best-k Search Algorithm for Neural Text Generation — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  70. Causal Reasoning of Entities and Events in Procedural Texts — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  71. Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  72. Complex Reasoning in Natural Language — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)
  73. Connectivity Patterns are Task Embeddings — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  74. Coverage-based Example Selection for In-Context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  75. Data-Efficient Finetuning Using Cross-Task Nearest Neighbors — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  76. Defining a New NLP Playground — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  77. DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning — 2023 · arXiv:2306.09030
  78. Do Question Answering Modeling Improvements Hold Across Benchmarks? — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  79. Domain Incremental Lifelong Learning in an Open World — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  80. Don''t Blame the Annotator: Bias Already Starts in the Annotation Instructions — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
  81. Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  82. Evaluating Open-Domain Question Answering in the Era of Large Language Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  83. Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  84. Extracting Victim Counts from Text — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
  85. FERMAT: An Alternative to Accuracy for Numerical Reasoning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  86. Few-shot Adaptation Works with UnpredicTable Data — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  87. Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  88. Fine-Tuning Language Models with Just Forward Passes — 2023 · arXiv:2305.17333
  89. GPQA: A Graduate-Level Google-Proof Q&A Benchmark — 2023 · arXiv:2311.12022
  90. Gemini: A Family of Highly Capable Multimodal Models — 2023 · arXiv:2312.11805
  91. How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  92. Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  93. In-context Pretraining: Language Modeling Beyond Document Boundaries — 2023 · arXiv:2310.10638
  94. InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining — 2023 · arXiv:2310.07713
  95. Intégration du raisonnement numérique dans les modèles de langue : État de l''art et direction de recherche — 2023 · Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications (CORIA)
  96. Large Language Models Can Be Easily Distracted by Irrelevant Context — 2023 · arXiv:2302.00093
  97. Large Language Models Can Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  98. Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners — 2023 · arXiv:2305.14825
  99. Large Language Models as Analogical Reasoners — 2023 · arXiv:2310.01714
  100. Learning to Generalize for Cross-domain QA — 2023 · Findings of the Association for Computational Linguistics: ACL 2023

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