---
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…"
canonical: "https://research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/cited-by/"
---
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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 140884 whose bibliographies we read) · 76 with a page here, the most cited first · page 1 of 4 · [← the paper](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)

**1** [2](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/cited-by/page/2/)

- [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) — Tom B. Brown, Benjamin Mann, Nick Ryder, et al. · 2020 · 10 782 citations
- [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/) — Long Ouyang, Jeff Wu, Xu Jiang, et al. · 2022 · 5 770 citations
- [Gemini: A Family of Highly Capable Multimodal Models](https://md.research.elamur.ai/free/en/papers/gemini-2023-team-gemini-family-highly-capable-multimodal/) — Gemini Team, Rohan Anil, Sebastian Borgeaud, et al. · 2023 · 2 951 citations
- [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) — Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2023 · 2 847 citations
- [PaLM: Scaling Language Modeling with Pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/) — Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al. · 2022 · 2 603 citations
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) — Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. · 2022 · 1 695 citations
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/) — Victor Sanh, Albert Webson, Colin Raffel, et al. · 2021 · 1 577 citations
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://md.research.elamur.ai/free/en/papers/gemini-2024-team-gemini-1-5-unlocking-multimodal/) — Gemini Team, Petko Georgiev, Ving Ian Lei, et al. · 2024 · 1 522 citations
- [Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/) — Emily M. Bender, Alexander Koller · 2020 · 1 371 citations
- [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) — Humza Naveed, Asad Ullah Khan, Shi Qiu, et al. · 2023 · 1 286 citations
- [Least-to-most prompting enables complex reasoning in large language models](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/) — 2022 · 1 143 citations
- [PaLM 2 Technical Report](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/) — Rohan Anil, Andrew M. Dai, Orhan Firat, et al. · 2023 · 971 citations
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/) — David Rein, Betty Li Hou, Asa Cooper Stickland, et al. · 2023 · 964 citations
- [TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/) — Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al. · 2020 · 844 citations
- [UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/) — Daniel Khashabi, Sewon Min, Tushar Khot, et al. · 2020 · 843 citations
- [Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) — Jiaxin Huang, Shixiang Gu, Le Hou, et al. · 2023 · 560 citations
- [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/) — Longhui Yu, Weisen Jiang, Han Shi, et al. · 2023 · 556 citations
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/) — Freda Shi, Xinyun Chen, Kanishka Misra, et al. · 2023 · 525 citations
- [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/) — Matt Gardner, Yoav Artzi, Victoria Basmov, et al. · 2020 · 520 citations
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/) — Zhiyu Chen, Wenhu Chen, Charese Smiley, et al. · 2021 · 510 citations
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/) — Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al. · 2020 · 479 citations
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/) — Nan Du, Yanping Huang, Andrew M. Dai, et al. · 2021 · 474 citations
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/) — Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al. · 2021 · 396 citations
- [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2020-logiqa-challenge-dataset-machine-reading/) — Jian Liu, Leyang Cui, Hanmeng Liu, et al. · 2020 · 395 citations
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/) — Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al. · 2024 · 379 citations

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