---
title: "Google DeepMind — works"
description: "29 works by researchers of Google DeepMind in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/companies/google-deepmind/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/companies/google-deepmind/works/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [companies](https://md.research.elamur.ai/free/en/companies/) / [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) / works

# Works of Google DeepMind

29 works · the most cited first · [← Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/)

1. [**Highly accurate protein structure prediction with AlphaFold**John Jumper, Richard Evans, Alexander Pritzel, et al.*2021 · 43,470 citations*](https://md.research.elamur.ai/free/en/papers/jumper-2021-highly-accurate-protein-structure-prediction/)
2. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
3. [**Large Language Models Encode Clinical Knowledge**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,464 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/)
4. [**Teaching Machines to Read and Comprehend**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,806 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
5. [**Stochastic Backpropagation and Approximate Inference in Deep Generative Models**Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra*2014 · 2,586 citations*](https://md.research.elamur.ai/free/en/papers/rezende-2014-stochastic-backpropagation-approximate-inference-deep/)
6. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
7. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
8. [**Competition-Level Code Generation with AlphaCode**Yujia Li, David Choi, Junyoung Chung, et al.*2022 · 1,543 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/)
9. [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,026 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
10. [**Universal and Transferable Adversarial Attacks on Aligned Language Models**Andy Zou, Zifan Wang, Nicholas Carlini, et al.*2023 · 951 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/)
11. [**Topic Modeling in Embedding Spaces**Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei*2020 · 914 citations*](https://md.research.elamur.ai/free/en/papers/dieng-2020-topic-modeling-embedding-spaces/)
12. [**MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer**Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, et al.*2020 · 831 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-mad-x-adapter-based-framework/)
13. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
14. [**Towards Expert-Level Medical Question Answering with Large Language Models**Karan Singhal, Tao Tu, Juraj Gottweis, et al.*2023 · 503 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/)
15. [**MasakhaNER: Named Entity Recognition for African Languages**David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, et al.*2021 · 429 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/)
16. [**Personality Traits in Large Language Models**Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.*2023 · 251 citations*](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
17. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
18. [**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/)
19. [**OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models**Anas Awadalla, Irena Gao, Josh Gardner, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/awadalla-2023-openflamingo-open-source-framework-training/)
20. [**Internet-augmented language models through few-shot prompting for open-domain question answering**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
21. [**Measuring Attribution in Natural Language Generation Models**Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/)
22. [**UNKs Everywhere: Adapting Multilingual Language Models to New Scripts**Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, et al.*2021 · 174 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2021-unks-everywhere-adapting-multilingual-language/)
23. [**SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training**Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/)
24. [**In-Context Learning Creates Task Vectors**Roee Hendel, Mor Geva, Amir Globerson*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/)
25. [**Machine Learning for Ancient Languages: A Survey**Thea Sommerschield, Yannis Assael, John Pavlopoulos, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/sommerschield-2023-machine-learning-ancient-languages-survey/)
26. [**V-STaR: Training Verifiers for Self-Taught Reasoners**Arian Hosseini, Xingdi Yuan, Nikolay Malkin, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2024-v-star-training-verifiers-self/)
27. [**WikiCREM: A Large Unsupervised Corpus for Coreference Resolution**Vid Kocijan, Oana-Maria Camburu, Ana-Maria Cretu, et al.*2019 · 41 citations*](https://md.research.elamur.ai/free/en/papers/kocijan-2019-wikicrem-large-unsupervised-corpus-coreference/)
28. [**Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
29. [**Ontology-Aware Token Embeddings for Prepositional Phrase Attachment**Pradeep Dasigi, Waleed Ammar, Chris Dyer, et al.*2017 · 35 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2017-ontology-aware-token-embeddings-prepositional/)
- [← back to](https://md.research.elamur.ai/free/en/companies/google-deepmind/) — Google DeepMind