---
title: "Who cites DROP: A Reading Comprehension Benchmark…"
description: "602 papers of the Elamur Research library cite «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs», found in their…"
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# Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs»

602 papers of our library cite it (of the 140,884 whose bibliographies we read) · 76 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)

1. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Training language models to follow instructions with human feedback**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 5,770 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
3. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
4. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
5. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
6. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
7. [**Multitask Prompted Training Enables Zero-Shot Task Generalization**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,577 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
8. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
9. [**Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data**Emily M. Bender, Alexander Koller*2020 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/)
10. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
11. [**Least-to-most prompting enables complex reasoning in large language models***2022 · 1,143 citations*](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/)
12. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
13. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
14. [**TaPas: Weakly Supervised Table Parsing via Pre-training**Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al.*2020 · 844 citations*](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/)
15. [**UNIFIEDQA: Crossing Format Boundaries with a Single QA System**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 843 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/)
16. [**Large Language Models Can Self-Improve**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 560 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/)
17. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 556 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
18. [**Large Language Models Can Be Easily Distracted by Irrelevant Context**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 525 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
19. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 520 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
20. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/)
21. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
22. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
23. [**TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/)
24. [**LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning**Jian Liu, Leyang Cui, Hanmeng Liu, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/liu-2020-logiqa-challenge-dataset-machine-reading/)
25. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
26. [**HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data**Wenhu Chen, Hanwen Zha, Zhiyu Chen, et al.*2020 · 376 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/)
27. [**A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers**Pradeep Dasigi, Kyle Lo, Iz Beltagy, et al.*2021 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/)
28. [**VILA: On Pre-training for Visual Language Models**Ji Lin, Hongxu Yin, Wei Ping, et al.*2023 · 367 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-language/)
29. [**TinyLlama: An Open-Source Small Language Model**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/)
30. [**Do NLP Models Know Numbers? Probing Numeracy in Embeddings**Eric Wallace, Yizhong Wang, Sujian Li, et al.*2019 · 348 citations*](https://md.research.elamur.ai/free/en/papers/wallace-2019-do-nlp-models-know-numbers/)
31. [**Kimi K2: Open Agentic Intelligence**Kimi Team, Yifan Bai, Yiping Bao, et al.*2025 · 331 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/)
32. [**SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 300 citations*](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/)
33. [**Muppet: Massive Multi-task Representations with Pre-Finetuning**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 299 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/)
34. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 283 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
35. [**Injecting Numerical Reasoning Skills into Language Models**Mor Geva, Ankit Gupta, Jonathan Berant*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/geva-2020-injecting-numerical-reasoning-skills-into/)
36. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
37. [**Can You Unpack That? Learning to Rewrite Questions-in-Context**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/)
38. [**Reframing Instructional Prompts to GPTk's Language**Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)
39. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
40. [**MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension**Alon Talmor, Jonathan Berant*2019 · 234 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-multiqa-empirical-investigation-generalization-transfer/)
41. [**Evaluating Open-Domain Question Answering in the Era of Large Language Models**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
42. [**A Discrete Hard EM Approach for Weakly Supervised Question Answering**Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
43. [**Break It Down: A Question Understanding Benchmark**Tomer Wolfson, Mor Geva, Ankit Gupta, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/)
44. [**Making Language Models Better Reasoners with Step-Aware Verifier**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/)
45. [**Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models**Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, et al.*2020 · 216 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-birds-have-four-legs-numersense/)
46. [**AlpaGasus: Training A Better Alpaca with Fewer Data**Lichang Chen, Shiyang Li, Jun Yan, et al.*2023 · 214 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/)
47. [**Training Question Answering Models From Synthetic Data**Raul Puri, Ryan Spring, Mohammad Shoeybi, et al.*2020 · 206 citations*](https://md.research.elamur.ai/free/en/papers/puri-2020-training-question-answering-models-synthetic/)
48. [**Towards Faithful Model Explanation in NLP: A Survey**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/)
49. [**Exploring and Predicting Transferability across NLP Tasks**Tu Vu, Tong Wang, Tsendsuren Munkhdalai, et al.*2020 · 192 citations*](https://md.research.elamur.ai/free/en/papers/vu-2020-exploring-predicting-transferability-across-nlp/)
50. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 191 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/)
51. [**MuTual: A Dataset for Multi-Turn Dialogue Reasoning**Leyang Cui, Yu Wu, Shujie Liu, et al.*2020 · 189 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-mutual-dataset-multi-turn-dialogue/)
52. [**LILA: A Unified Benchmark for Mathematical Reasoning**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/)
53. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 171 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
54. [**ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering**Zhiyu Chen, Shiyang Li, Charese Smiley, et al.*2022 · 171 citations*](https://md.research.elamur.ai/free/en/papers/chen-2022-convfinqa-exploring-chain-numerical-reasoning/)
55. [**Reasoning Over Paragraph Effects in Situations**Kevin Lin, Oyvind Tafjord, Peter Clark, et al.*2019 · 171 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-reasoning-over-paragraph-effects-situations/)
56. [**Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/)
57. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
58. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
59. [**ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning**Olga Golovneva, Moya Chen, Spencer Poff, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/golovneva-2022-roscoe-suite-metrics-scoring-step/)
60. [**TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions**Qiang Ning, Hao Wu, Rujun Han, et al.*2020 · 149 citations*](https://md.research.elamur.ai/free/en/papers/ning-2020-torque-reading-comprehension-dataset-temporal/)
61. [**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)
62. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/)
63. [**Efficient Training of Language Models to Fill in the Middle**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 137 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
64. [**Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention**Jingyang Yuan, Huazuo Gao, Damai Dai, et al.*2025 · 135 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2025-native-sparse-attention-hardware/)
65. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
66. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 130 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
67. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
68. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 117 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/)
69. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
70. [**SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning**Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning, et al.*2021 · 92 citations*](https://md.research.elamur.ai/free/en/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/)
71. [**Are Pretrained Language Models Symbolic Reasoners over Knowledge?**Nora Kassner, Benno Krojer, Hinrich Schütze*2020 · 79 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-are-pretrained-language-models-symbolic/)
72. [**English Machine Reading Comprehension Datasets: A Survey**Daria Dzendzik, Jennifer Foster, Carl Vogel*2021 · 53 citations*](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
73. [**Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data**Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/akhtar-2023-exploring-numerical-reasoning-capabilities-language/)
74. [**Coreference Reasoning in Machine Reading Comprehension**Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, et al.*2021 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-coreference-reasoning-machine-reading-comprehension/)
75. [**A guide to the dataset explosion in QA, NLI, and commonsense reasoning**Anna Rogers, Anna Rumshisky*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
76. [**MEUR: A Benchmark for Evaluating Vision-Language Models on Multimodal Event Understanding and Reasoning**Zimu Wang, Yuqi Wang, Tong Chen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-meur-benchmark-evaluating-vision-language/)

526 more papers of our library cite it; they have no page here yet.

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