Zettlemoyer, Luke

LLM.int8(): 8-Bit Matrix Multiplication for Transformers at Scale

Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022) · Advances in Neural Information Processing Systems 35

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 305

Generalization through Memorization: Nearest Neighbor Language Models

Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 285

LIMA: Less Is More for Alignment

Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al. (2023) · Advances in Neural Information Processing Systems 36

Domain Adaptation and Few-Shot LearningEducational Assessment and Pedagogy

Cited by 275

LIMA: Less Is More for Alignment

Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningEducational Assessment and Pedagogy

Cited by 275

A Joint Model of Language and Perception for Grounded Attribute Learning

Matuszek, C., FitzGerald, N., Zettlemoyer, L., Bo, L., & Fox, D. (2012) · Maryland Shared Open Access Repository

Categorization, perception, and languageRobot Manipulation and Learning

Cited by 248

Efficient Large Scale Language Modeling with Mixtures of Experts

Artetxe, M., Bhosale, S., Goyal, N., Mihaylov, T., Ott, M., Shleifer, S., Lin, X. V., Du, J., Iyer, S., Pasunuru, R., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 223

Cloze-driven Pretraining of Self-attention Networks

Baevski, A., Edunov, S., Liu, Y., Zettlemoyer, L., & Auli, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 215

Cloze-driven Pretraining of Self-attention Networks

Baevski, A., Edunov, S., Liu, Y., Zettlemoyer, L., & Auli, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 215

REPLUG: Retrieval-Augmented Black-Box Language Models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 209

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 197

InCoder: A Generative Model for Code Infilling and Synthesis

Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-T., Zettlemoyer, L., & Lewis, M. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 187

Muppet: Massive Multi-task Representations with Pre-Finetuning

Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 183

Mapping Language to Code in Programmatic Context

Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Software Engineering ResearchTopic Modeling

Cited by 180

Vision-and-dialog navigation

Thomason, J., Murray, M., Cakmak, M., & Zettlemoyer, L. (2019) · arXiv

Speech and dialogue systemsRobot Manipulation and Learning

Cited by 173

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 169

Nearest Neighbor Machine Translation

Khandelwal, U., Fan, A., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2020) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 165

NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System

Lin, X. V., Wang, C., Zettlemoyer, L., & Ernst, M. D. (2018) · arXiv

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 144

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., & Zettlemoyer, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingInformation Retrieval and Search Behavior

Cited by 138

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 127

Detecting Hallucinated Content in Conditional Neural Sequence Generation

Zhou, C., Neubig, G., Gu, J., Diab, M., Guzmán, F., Zettlemoyer, L., & Ghazvininejad, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Natural Language Processing TechniquesTopic Modeling

Cited by 122

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

Wang, B., Min, S., Deng, X., Shen, J., Wu, Y., Zettlemoyer, L., & Sun, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 120

Demystifying Prompts in Language Models via Perplexity Estimation

Gonen, H., Iyer, S., Blevins, T., Smith, N., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 109

Demystifying Prompts in Language Models via Perplexity Estimation

Gonen, H., Iyer, S., Blevins, T., Smith, N. A., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 109

Improving Passage Retrieval with Zero-Shot Question Generation

Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Information Retrieval and Search BehaviorTopic Modeling

Cited by 90