---
title: "University of California, Berkeley — works"
description: "107 works by researchers of University of California, Berkeley in our library · page 1 of 2, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/university-of-california-berkeley/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/universities/university-of-california-berkeley/works/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [universities](https://md.research.elamur.ai/free/en/universities/) / [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) / works

# Works of University of California, Berkeley

107 works · page 1 of 2 · the most cited first · [← University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/)

**1** [2](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/works/page/2/) [next →](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/works/page/2/)

1. [**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/)
2. [**Rich feature hierarchies for accurate object detection and semantic segmentation**Ross Girshick, Jeff Donahue, Trevor Darrell, et al.*2013 · 31,715 citations*](https://md.research.elamur.ai/free/en/papers/girshick-2013-rich-feature-hierarchies-accurate-object/)
3. [**Latent Dirichlet Allocation**David M. Blei, Andrew Y. Ng, Michael I. Jordan*2003 · 28,297 citations*](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
4. [**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/)
5. [**Bagging Predictors**Leo Breiman*1996 · 16,493 citations*](https://md.research.elamur.ai/free/en/papers/breiman-1996-bagging-predictors/)
6. [**Politeness: Some Universals in Language Usage**Penelope Brown, Stephen C. Levinson, John J. Gumperz*1987 · 10,545 citations*](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/)
7. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,064 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
8. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
9. [**Scalable Diffusion Models with Transformers**William Peebles, Saining Xie*2022 · 2,595 citations*](https://md.research.elamur.ai/free/en/papers/peebles-2022-scalable-diffusion-models-transformers/)
10. [**Lost in the Middle: How Language Models Use Long Contexts**Nelson F. Liu, Kevin Lin, John Hewitt, et al.*2023 · 2,540 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/)
11. [**Measuring Mathematical Problem Solving With the MATH Dataset**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,318 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/)
12. [**Automatic Labeling of Semantic Roles**Daniel Gildea, Daniel Jurafsky*2002 · 2,216 citations*](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/)
13. [**Probabilistic Latent Semantic Analysis**Thomas Hofmann*1999 · 2,145 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-1999-probabilistic-latent-semantic-analysis/)
14. [**AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts**Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, et al.*2020 · 1,879 citations*](https://md.research.elamur.ai/free/en/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/)
15. [**Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding**Akira Fukui, Dong Huk Park, Daylen Yang, et al.*2016 · 1,642 citations*](https://md.research.elamur.ai/free/en/papers/fukui-2016-multimodal-compact-bilinear-pooling-visual/)
16. [**Deep Unsupervised Learning using Nonequilibrium Thermodynamics**Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, et al.*2015 · 1,588 citations*](https://md.research.elamur.ai/free/en/papers/sohl-dickstein-2015-deep-unsupervised-learning-using-nonequilibrium/)
17. [**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/)
18. [**Hedges: A Study in Meaning Criteria and the Logic of Fuzzy Concepts**George Lakoff*1973 · 1,486 citations*](https://md.research.elamur.ai/free/en/papers/lakoff-1973-hedges-study-meaning-criteria/)
19. [**Learning Accurate, Compact, and Interpretable Tree Annotation**Slav Petrov, Leon Barrett, Romain Thibaux, et al.*2006 · 1,242 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2006-learning-accurate-compact-interpretable-tree/)
20. [**Neural Module Networks**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2015 · 1,075 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2015-neural-module-networks/)
21. [**Improved Inference for Unlexicalized Parsing**Slav Petrov, Dan Klein*2007 · 941 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2007-improved-inference-unlexicalized-parsing/)
22. [**Extracting Training Data from Large Language Models**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 901 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
23. [**Calibrate Before Use: Improving Few-Shot Performance of Language Models**Tony Z. Zhao, Eric Wallace, Shi Feng, et al.*2021 · 791 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/)
24. [**Reformer: The Efficient Transformer**Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya*2020 · 774 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2020-reformer-efficient-transformer/)
25. [**Constituency Parsing with a Self-Attentive Encoder**Nikita Kitaev, Dan Klein*2018 · 759 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2018-constituency-parsing-self-attentive-encoder/)
26. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
27. [**Crosslingual Generalization through Multitask Finetuning**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 692 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
28. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 668 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/)
29. [**Alignment by Agreement**Percy Liang, Ben Taskar, Dan Klein*2006 · 652 citations*](https://md.research.elamur.ai/free/en/papers/liang-2006-alignment-agreement/)
30. [**Exploring Content Models for Multi-Document Summarization**Aria Haghighi, Lucy Vanderwende*2009 · 630 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2009-exploring-content-models-multi-document/)
31. [**Object Hallucination in Image Captioning**Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, et al.*2018 · 617 citations*](https://md.research.elamur.ai/free/en/papers/rohrbach-2018-object-hallucination-image-captioning/)
32. [**A Critique and Improvement of an Evaluation Metric for Text Segmentation**Lev Pevzner, Marti A. Hearst*2002 · 594 citations*](https://md.research.elamur.ai/free/en/papers/pevzner-2002-critique-improvement-evaluation-metric-text/)
33. [**Learning to Compose Neural Networks for Question Answering**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2016 · 547 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/)
34. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code**Naman Jain, King Han, Alex Gu, et al.*2024 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
35. [**Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters**Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.*2024 · 524 citations*](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/)
36. [**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/)
37. [**Learning Semantic Correspondences with Less Supervision**Percy Liang, Michael Jordan, Dan Klein*2009 · 455 citations*](https://md.research.elamur.ai/free/en/papers/liang-2009-learning-semantic-correspondences-less-supervision/)
38. [**Pretrained Transformers Improve Out-of-Distribution Robustness**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 442 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/)
39. [**Measuring Coding Challenge Competence With APPS**Dan Hendrycks, Steven Basart, Saurav Kadavath, et al.*2021 · 438 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-coding-challenge-competence-apps/)
40. [**Learning Bilingual Lexicons from Monolingual Corpora**Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, et al.*2008 · 426 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2008-learning-bilingual-lexicons-monolingual-corpora/)
41. [**Offline bilingual word vectors, orthogonal transformations and the inverted softmax**Samuel L. Smith, David H. P. Turban, Steven Hamblin, et al.*2017 · 426 citations*](https://md.research.elamur.ai/free/en/papers/smith-2017-offline-bilingual-word-vectors-orthogonal/)
42. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
43. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
44. [**Aligning AI With Shared Human Values**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 382 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/)
45. [**Gender and Representation Bias in GPT-3 Generated Stories**Li Lucy, David Bamman*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/lucy-2021-gender-representation-bias-gpt-3/)
46. [**A fully Bayesian approach to unsupervised part-of-speech tagging**Sharon Goldwater, Tom Griffiths*2007 · 380 citations*](https://md.research.elamur.ai/free/en/papers/goldwater-2007-fully-bayesian-approach-unsupervised-part/)
47. [**A Scalable Global Model for Summarization**Dan Gillick, Benoit Favre*2009 · 376 citations*](https://md.research.elamur.ai/free/en/papers/gillick-2009-scalable-global-model-summarization/)
48. [**Multilingual Constituency Parsing with Self-Attention and Pre-Training**Nikita Kitaev, Steven Cao, Dan Klein*2019 · 356 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2019-multilingual-constituency-parsing-self-attention/)
49. [**Bayesian Inference for PCFGs via Markov Chain Monte Carlo**Mark Johnson, Thomas Griffiths, Sharon Goldwater*2007 · 352 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2007-bayesian-inference-pcfgs-markov-chain/)
50. [**Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages**Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, et al.*2020 · 333 citations*](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)
51. [**A Joint Model for Entity Analysis: Coreference, Typing, and Linking**Greg Durrett, Dan Klein*2014 · 329 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2014-joint-model-entity-analysis-coreference/)
52. [**A Minimal Span-Based Neural Constituency Parser**Mitchell Stern, Jacob Andreas, Dan Klein*2017 · 309 citations*](https://md.research.elamur.ai/free/en/papers/stern-2017-minimal-span-based-neural-constituency/)
53. [**Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs**Shengbang Tong, Zhuang Liu, Yuexiang Zhai, et al.*2024 · 297 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-eyes-wide-shut-exploring-visual/)
54. [**Easy Victories and Uphill Battles in Coreference Resolution**Greg Durrett, Dan Klein*2013 · 293 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2013-easy-victories-uphill-battles-coreference/)
55. [**Simple Coreference Resolution with Rich Syntactic and Semantic Features**Aria Haghighi, Dan Klein*2009 · 277 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/)
56. [**CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review**Dan Hendrycks, Collin Burns, Anya Chen, et al.*2021 · 265 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-cuad-expert-annotated-nlp-dataset/)
57. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
58. [**A Simple Domain-Independent Probabilistic Approach to Generation**Gabor Angeli, Percy Liang, Dan Klein*2010 · 260 citations*](https://md.research.elamur.ai/free/en/papers/angeli-2010-simple-domain-independent-probabilistic-approach/)
59. [**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/)
60. [**A Discriminative Matching Approach to Word Alignment**Ben Taskar, Simon Lacoste-Julien, Dan Klein*2005 · 237 citations*](https://md.research.elamur.ai/free/en/papers/taskar-2005-discriminative-matching-approach-word-alignment/)
61. [**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/)
62. [**LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 235 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-lmsys-chat-1m-large-scale/)
63. [**FUDGE: Controlled Text Generation With Future Discriminators**Kevin Yang, Dan Klein*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
64. [**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/)
65. [**Coreference Resolution in a Modular, Entity-Centered Model**Aria Haghighi, Dan Klein*2010 · 215 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2010-coreference-resolution-modular-entity-centered/)
66. [**Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models**Robert Logan IV, Ivana Balazevic, Eric Wallace, et al.*2022 · 215 citations*](https://md.research.elamur.ai/free/en/papers/iv-2022-cutting-down-prompts-parameters-simple/)
67. [**A Global Joint Model for Semantic Role Labeling**Kristina Toutanova, Aria Haghighi, Christopher D. Manning*2008 · 214 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2008-global-joint-model-semantic-role/)
68. [**Gradient Surgery for Multi-Task Learning**Tianhe Yu, Saurabh Kumar, Abhishek Gupta, et al.*2020 · 214 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-gradient-surgery-multi-task-learning/)
69. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
70. [**Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints**Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein*2016 · 209 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2016-learning-based-single-document-summarization/)
71. [**Ring Attention with Blockwise Transformers for Near-Infinite Context**Hao Liu, Matei Zaharia, Pieter Abbeel*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-ring-attention-blockwise-transformers-near/)
72. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
73. [**Supervised Learning of Complete Morphological Paradigms**Greg Durrett, John DeNero*2013 · 205 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2013-morphological-paradigms/)
74. [**Reasoning about Pragmatics with Neural Listeners and Speakers**Jacob Andreas, Dan Klein*2016 · 198 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2016-reasoning-about-pragmatics-neural-listeners/)
75. [**Scalable Extraction of Training Data from (Production) Language Models**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 197 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
76. [**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/)
77. [**OpenHands: An Open Platform for AI Software Developers as Generalist Agents**Xingyao Wang, Boxuan Li, Yufan Song, et al.*2024 · 190 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-openhands-open-platform-ai/)
78. [**SeamlessM4T: Massively Multilingual & Multimodal Machine Translation**Seamless Communication, Loic Barrault, Yu-An Chung, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/communication-2023-seamlessm4t-massively-multilingual-multimodal-machine/)
79. [**Unsupervised Coreference Resolution in a Nonparametric Bayesian Model**Aria Haghighi, Dan Klein*2007 · 182 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2007-unsupervised-coreference-resolution-nonparametric-bayesian/)
80. [**Mesh-TensorFlow: Deep Learning for Supercomputers**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 182 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/)
81. [**Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy**Barbara Rosario, Marti Hearst*2001 · 180 citations*](https://md.research.elamur.ai/free/en/papers/rosario-2001-classifying-semantic-relations-noun-compounds/)
82. [**The False Promise of Imitating Proprietary LLMs**Arnav Gudibande, Eric Wallace, Charlie Snell, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/gudibande-2023-false-promise-imitating-proprietary-llms/)
83. [**Seamless: Multilingual Expressive and Streaming Speech Translation**Seamless Communication, Loic Barrault, Yu-An Chung, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/communication-2023-seamless-multilingual-expressive-streaming-speech/)
84. [**SqueezeLLM: Dense-and-Sparse Quantization**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
85. [**Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4**Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.*2023 · 166 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-speak-memory-archaeology-books-known/)
86. [**Better Word Alignments with Supervised ITG Models**Aria Haghighi, John Blitzer, John DeNero, et al.*2009 · 163 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2009-better-word-alignments-supervised-itg/)
87. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
88. [**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/)
89. [**RAFT: Adapting Language Model to Domain Specific RAG**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/)
90. [**Parser Showdown at the Wall Street Corral: An Empirical Investigation of Error Types in Parser Output**Jonathan K. Kummerfeld, David Hall, James R. Curran, et al.*2012 · 127 citations*](https://md.research.elamur.ai/free/en/papers/kummerfeld-2012-parser-showdown-wall-street-corral/)
91. [**Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning**Ruiqi Zhang, Licong Lin, Yu Bai, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-negative-preference-optimization-catastrophic-collapse/)
92. [**I-BERT: Integer-only BERT Quantization**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 118 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/)
93. [**Unified Pragmatic Models for Generating and Following Instructions**Daniel Fried, Jacob Andreas, Dan Klein*2018 · 111 citations*](https://md.research.elamur.ai/free/en/papers/fried-2018-unified-pragmatic-models-generating-following/)
94. [**Sentence Boundary Detection and the Problem with the U.S.**Dan Gillick*2009 · 109 citations*](https://md.research.elamur.ai/free/en/papers/gillick-2009-sentence-boundary-detection-problem-u/)
95. [**The FrameNet Database and Software Tools**Charles J. Fillmore, Collin F. Baker, Hiroaki Sato*2002 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fillmore-2002-framenet-database-software-tools/)
96. [**An Annotated Dataset of Coreference in English Literature**David Bamman, Olivia Lewke, Anya Mansoor*2020 · 103 citations*](https://md.research.elamur.ai/free/en/papers/bamman-2020-annotated-dataset-coreference-english-literature/)
97. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
98. [**Adaptive Sentence Boundary Disambiguation**David D. Palmer, Marti A. Hearst*1994 · 91 citations*](https://md.research.elamur.ai/free/en/papers/palmer-1994-adaptive-sentence-boundary-disambiguation/)
99. [**Error-Driven Analysis of Challenges in Coreference Resolution**Jonathan K. Kummerfeld, Dan Klein*2013 · 83 citations*](https://md.research.elamur.ai/free/en/papers/kummerfeld-2013-error-driven-analysis-challenges-coreference/)
100. [**Pragmatically Informative Text Generation**Sheng Shen, Daniel Fried, Jacob Andreas, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/shen-2019-pragmatically-informative-text-generation/)

**1** [2](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/works/page/2/) [next →](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/works/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University of California, Berkeley