[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-2/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/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](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-6/)

1. [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://arxiv.org/abs/2401.02954) *— 2024 · arXiv:2401.02954*
2. [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) *— 2024 · arXiv:2405.04434*
3. [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) *— 2024 · arXiv:2412.19437*
4. [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)*
5. [Divide-or-Conquer? Which Part Should You Distill Your LLM?](https://aclanthology.org/2024.findings-emnlp.145/) *— 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](https://aclanthology.org/2024.acl-long.852/) *— 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](https://aclanthology.org/2024.findings-emnlp.821/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
8. [Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://arxiv.org/abs/2403.09472) *— 2024 · arXiv:2403.09472*
9. [EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning](https://aclanthology.org/2024.naacl-short.35/) *— 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](https://aclanthology.org/2024.findings-naacl.132/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
11. [Eureka: Evaluating and Understanding Large Foundation Models](https://arxiv.org/abs/2409.10566) *— 2024 · arXiv:2409.10566*
12. [FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition](https://aclanthology.org/2024.emnlp-main.734/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
13. [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://aclanthology.org/2024.emnlp-main.15/) *— 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](https://arxiv.org/abs/2406.11939) *— 2024 · arXiv:2406.11939*
15. [Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap](https://arxiv.org/abs/2402.19450) *— 2024 · arXiv:2402.19450*
16. [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*
17. [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464) *— 2024 · arXiv:2412.06464*
18. [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://arxiv.org/abs/2403.05530) *— 2024 · arXiv:2403.05530*
19. [Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent](https://arxiv.org/abs/2411.02265) *— 2024 · arXiv:2411.02265*
20. [Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset](https://aclanthology.org/2024.naacl-long.248/) *— 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](https://aclanthology.org/2024.acl-long.612/) *— 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](https://aclanthology.org/2024.lrec-main.774/) *— 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](https://arxiv.org/abs/2402.05403) *— 2024 · arXiv:2402.05403*
24. [JDocQA: Japanese Document Question Answering Dataset for Generative Language Models](https://aclanthology.org/2024.lrec-main.830/) *— 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](https://arxiv.org/abs/2407.05483) *— 2024 · arXiv:2407.05483*
26. [Knowledge Fusion of Large Language Models](https://arxiv.org/abs/2401.10491) *— 2024 · arXiv:2401.10491*
27. [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://arxiv.org/abs/2406.18403) *— 2024 · arXiv:2406.18403*
28. [Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://aclanthology.org/2024.findings-eacl.26/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
29. [Lessons from the Trenches on Reproducible Evaluation of Language Models](https://arxiv.org/abs/2405.14782) *— 2024 · arXiv:2405.14782*
30. [LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models](https://aclanthology.org/2024.naacl-long.174/) *— 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](https://aclanthology.org/2024.acl-long.739/) *— 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](https://arxiv.org/abs/2412.15204) *— 2024 · arXiv:2412.15204*
33. [MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning](https://aclanthology.org/2024.acl-long.168/) *— 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](https://aclanthology.org/2024.findings-acl.872/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
35. [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*
36. [MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures](https://arxiv.org/abs/2406.06565) *— 2024 · arXiv:2406.06565*
37. [Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?](https://aclanthology.org/2024.acl-long.304/) *— 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?](https://aclanthology.org/2024.insights-1.18/) *— 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](https://aclanthology.org/2024.acl-long.225/) *— 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](https://arxiv.org/abs/2411.03766) *— 2024 · arXiv:2411.03766*
41. [ODIN: Disentangled Reward Mitigates Hacking in RLHF](https://arxiv.org/abs/2402.07319) *— 2024 · arXiv:2402.07319*
42. [PEDANTS: Cheap but Effective and Interpretable Answer Equivalence](https://aclanthology.org/2024.findings-emnlp.548/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
43. [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)*
44. [Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models](https://arxiv.org/abs/2411.12580) *— 2024 · arXiv:2411.12580*
45. [Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks](https://aclanthology.org/2024.findings-acl.743/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
46. [Rationale-Aware Answer Verification by Pairwise Self-Evaluation](https://aclanthology.org/2024.emnlp-main.905/) *— 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](https://arxiv.org/abs/2403.11793) *— 2024 · arXiv:2403.11793*
48. [Revisiting Knowledge Distillation for Autoregressive Language Models](https://aclanthology.org/2024.acl-long.587/) *— 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](https://arxiv.org/abs/2404.05399) *— 2024 · arXiv:2404.05399*
50. [SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers](https://aclanthology.org/2024.emnlp-main.1163/) *— 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?](https://aclanthology.org/2024.emnlp-main.147/) *— 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](https://aclanthology.org/2024.findings-naacl.175/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
53. [Synthetic Multimodal Question Generation](https://aclanthology.org/2024.findings-emnlp.759/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
54. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
55. [TinyLlama: An Open-Source Small Language Model](https://arxiv.org/abs/2401.02385) *— 2024 · arXiv:2401.02385*
56. [Towards Faithful Model Explanation in NLP: A Survey](https://aclanthology.org/2024.cl-2.6/) *— 2024 · Computational Linguistics*
57. [Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond](https://aclanthology.org/2024.findings-acl.493/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
58. [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124) *— 2024 · arXiv:2411.15124*
59. [Unified Active Retrieval for Retrieval Augmented Generation](https://aclanthology.org/2024.findings-emnlp.999/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
60. [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)*
61. [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://aclanthology.org/2024.findings-acl.844/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
62. [A Comprehensive Evaluation of Tool-Assisted Generation Strategies](https://aclanthology.org/2023.findings-emnlp.926/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
63. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
64. [A Survey of Deep Learning for Mathematical Reasoning](https://aclanthology.org/2023.acl-long.817/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) *— 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)*
66. [AlpaGasus: Training A Better Alpaca with Fewer Data](https://arxiv.org/abs/2307.08701) *— 2023 · arXiv:2307.08701*
67. [Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering](https://aclanthology.org/2023.findings-eacl.87/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
68. [Arithmetic-Based Pretraining Improving Numeracy of Pretrained Language Models](https://aclanthology.org/2023.starsem-1.42/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
69. [Best-k Search Algorithm for Neural Text Generation](https://aclanthology.org/2023.acl-long.692/) *— 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](https://aclanthology.org/2023.findings-eacl.31/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
71. [Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering](https://aclanthology.org/2023.emnlp-main.547/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
72. [Complex Reasoning in Natural Language](https://aclanthology.org/2023.acl-tutorials.2/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)*
73. [Connectivity Patterns are Task Embeddings](https://aclanthology.org/2023.findings-acl.759/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
74. [Coverage-based Example Selection for In-Context Learning](https://aclanthology.org/2023.findings-emnlp.930/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
75. [Data-Efficient Finetuning Using Cross-Task Nearest Neighbors](https://aclanthology.org/2023.findings-acl.576/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
76. [Defining a New NLP Playground](https://aclanthology.org/2023.findings-emnlp.799/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
77. [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) *— 2023 · arXiv:2306.09030*
78. [Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://aclanthology.org/2023.acl-long.736/) *— 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](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
80. [Don''t Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://aclanthology.org/2023.eacl-main.130/) *— 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](https://aclanthology.org/2023.acl-long.396/) *— 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](https://aclanthology.org/2023.acl-long.307/) *— 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](https://aclanthology.org/2023.findings-emnlp.1028/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
84. [Extracting Victim Counts from Text](https://aclanthology.org/2023.eacl-main.141/) *— 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](https://aclanthology.org/2023.acl-long.838/) *— 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](https://aclanthology.org/2023.acl-long.102/) *— 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](https://aclanthology.org/2023.findings-acl.833/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
88. [Fine-Tuning Language Models with Just Forward Passes](https://arxiv.org/abs/2305.17333) *— 2023 · arXiv:2305.17333*
89. [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://arxiv.org/abs/2311.12022) *— 2023 · arXiv:2311.12022*
90. [Gemini: A Family of Highly Capable Multimodal Models](https://arxiv.org/abs/2312.11805) *— 2023 · arXiv:2312.11805*
91. [How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension](https://aclanthology.org/2023.findings-acl.359/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
92. [Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task](https://aclanthology.org/2023.findings-eacl.4/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
93. [In-context Pretraining: Language Modeling Beyond Document Boundaries](https://arxiv.org/abs/2310.10638) *— 2023 · arXiv:2310.10638*
94. [InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining](https://arxiv.org/abs/2310.07713) *— 2023 · arXiv:2310.07713*
95. [Intégration du raisonnement numérique dans les modèles de langue : État de l''art et direction de recherche](https://aclanthology.org/2023.jeptalnrecital-coria.11/) *— 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](https://arxiv.org/abs/2302.00093) *— 2023 · arXiv:2302.00093*
97. [Large Language Models Can Self-Improve](https://aclanthology.org/2023.emnlp-main.67/) *— 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](https://arxiv.org/abs/2305.14825) *— 2023 · arXiv:2305.14825*
99. [Large Language Models as Analogical Reasoners](https://arxiv.org/abs/2310.01714) *— 2023 · arXiv:2310.01714*
100. [Learning to Generalize for Cross-domain QA](https://aclanthology.org/2023.findings-acl.84/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-6/)

- [← previous Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring/)
- [next → Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-3/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)