---
title: "University of Oxford — works"
description: "39 works by researchers of University of Oxford in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/university-of-oxford/works/"
---
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# Works of University of Oxford

39 works · the most cited first · [← University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/)

1. [**Very Deep Convolutional Networks for Large-Scale Image Recognition**Karen Simonyan, Andrew Zisserman*2014 · 72,803 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/)
2. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
3. [**Array Programming with NumPy**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,591 citations*](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/)
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. [**TruthfulQA: Measuring How Models Mimic Human Falsehoods**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,047 citations*](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/)
6. [**Recurrent Continuous Translation Models**Nal Kalchbrenner, Phil Blunsom*2013 · 1,597 citations*](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-continuous-translation-models/)
7. [**Generation and Comprehension of Unambiguous Object Descriptions**Junhua Mao, Jonathan Huang, Alexander Toshev, et al.*2015 · 1,358 citations*](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/)
8. [**Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps**Karen Simonyan, Andrea Vedaldi, Andrew Zisserman*2013 · 1,153 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2013-deep-inside-convolutional-networks-visualising/)
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. [**A Convolutional Neural Network for Modelling Sentences**Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom*2014 · 824 citations*](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2014-convolutional-neural-network-modelling-sentences/)
11. [**Learning multiple visual domains with residual adapters**Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi*2017 · 670 citations*](https://md.research.elamur.ai/free/en/papers/rebuffi-2017-learning-multiple-visual-domains-residual/)
12. [**Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models**Stephen Clark, James R. Curran*2007 · 625 citations*](https://md.research.elamur.ai/free/en/papers/clark-2007-wide-coverage-efficient-statistical-parsing/)
13. [**e-SNLI: Natural Language Inference with Natural Language Explanations**Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, et al.*2018 · 612 citations*](https://md.research.elamur.ai/free/en/papers/camburu-2018-e-snli-natural-language-inference/)
14. [**Multilingual Models for Compositional Distributed Semantics**Karl Moritz Hermann, Phil Blunsom*2014 · 375 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2014-multilingual-models-compositional-distributed-semantics/)
15. [**cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models**Chris Dyer, Adam Lopez, Juri Ganitkevitch, et al.*2010 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dyer-2010-cdec-decoder-alignment-learning-framework/)
16. [**A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing**Yue Zhang, Stephen Clark*2008 · 336 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2008-tale-two-parsers-investigating-combining/)
17. [**Recurrent Convolutional Neural Networks for Discourse Compositionality**Nal Kalchbrenner, Phil Blunsom*2013 · 253 citations*](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-convolutional-neural-networks-discourse/)
18. [**The Curse of Recursion: Training on Generated Data Makes Models Forget**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
19. [**Factuality Challenges in the Era of Large Language Models**Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)
20. [**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/)
21. [**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/)
22. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
23. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
24. [**Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks**Paul Röttger, Bertie Vidgen, Dirk Hovy, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2022-two-contrasting-data-annotation-paradigms/)
25. [**A Classifier-Based Approach to Preposition and Determiner Error Correction in L2 English**Rachele De Felice, Stephen G. Pulman*2008 · 202 citations*](https://md.research.elamur.ai/free/en/papers/felice-2008-classifier-based-approach-preposition-determiner/)
26. [**HateCheck: Functional Tests for Hate Speech Detection Models**Paul Röttger, Bertie Vidgen, Dong Nguyen, et al.*2021 · 202 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/)
27. [**SemEval-2023 Task 10: Explainable Detection of Online Sexism**Hannah Kirk, Wenjie Yin, Bertie Vidgen, et al.*2023 · 185 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-semeval-2023-task-10-explainable/)
28. [**Semantics-Based Machine Translation with Hyperedge Replacement Grammars**Bevan Jones, Jacob Andreas, Daniel Bauer, et al.*2012 · 180 citations*](https://md.research.elamur.ai/free/en/papers/jones-2012-semantics-based-machine-translation-hyperedge/)
29. [**Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model**Yue Zhang, Stephen Clark*2009 · 151 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2009-transition-based-parsing-chinese-treebank/)
30. [**Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models**Paul Röttger, Valentin Hofmann, Valentina Pyatkin, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-political-compass-or-spinning-arrow/)
31. [**Experimenting with the king of France: Topics, verifiability and definite descriptions**Márta Abrusán, Kriszta Szendrői*2013 · 132 citations*](https://md.research.elamur.ai/free/en/papers/abrusan-2013-experimenting-king-france-topics-verifiability/)
32. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
33. [**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/)
34. [**Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words**Valentin, Hofmann*2021 · 101 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/)
35. [**Inverse Scaling: When Bigger Isn't Better**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
36. [**Formalism-Independent Parser Evaluation with CCG and DepBank**Stephen Clark, James Curran*2007 · 79 citations*](https://md.research.elamur.ai/free/en/papers/clark-2007-formalism-independent-parser-evaluation-ccg/)
37. [**Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model**Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-chatgpt-wugs-morphology/)
38. [**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/)
39. [**The participle as a component of the verbal system in Biblical Hebrew**Jan Joosten*2020 · 2 citations*](https://md.research.elamur.ai/free/en/papers/joosten-2020-participle-component-verbal-system-biblical/)
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