Zettlemoyer, Luke

BASE Layers: Simplifying Training of Large, Sparse Models

Lewis, M., Bhosale, S., Dettmers, T., Goyal, N., & Zettlemoyer, L. (2021) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 88

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

Iyer, S., Lin, X. V., Pasunuru, R., Mihaylov, T., Simig, D., Yu, P., Shuster, K., Wang, T., Liu, Q., Koura, P. S., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 81

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candes, E., & Hashimoto, T. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 76

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Selective annotation makes language models better few-shot learners

Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 70

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Tirumala, K., Markosyan, A., Zettlemoyer, L., & Aghajanyan, A. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 67

REPLUG: Retrieval-augmented black-box language models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 65

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 61

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

Binding Language Models in Symbolic Languages

Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

Natural Language to Code Translation with Execution

Shi, F., Fried, D., Ghazvininejad, M., Zettlemoyer, L., & Wang, S. I. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 49

The case for 4-bit precision: k-bit Inference Scaling Laws

Dettmers, T., & Zettlemoyer, L. (2022) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 46

Memory Layers at Scale

Berges, V.-P., Oğuz, B., Haziza, D., Yih, W.-T., Zettlemoyer, L., & Ghosh, G. (2024) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 46

CM3: A Causal Masked Multimodal Model of the Internet

Aghajanyan, A., Huang, B., Ross, C., Karpukhin, V., Xu, H., Goyal, N., Okhonko, D., Joshi, M., Ghosh, G., Lewis, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 42

Synthesizing scientific literature with retrieval-augmented language models

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., et al. (2026) · Nature

Topic ModelingInformation Retrieval and Search Behavior

Cited by 38

RevMiner: an extractive interface for navigating reviews on a smartphone

Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., & Lee, C. (2012) · Proceedings of the 25th annual ACM symposium on User interface software and technology

Data Visualization and AnalyticsInformation Retrieval and Search Behavior

Cited by 37

Detecting Pretraining Data from Large Language Models

Shi, W., Ajith, A., Xia, M., Huang, Y., Liu, D., Blevins, T., Chen, D., & Zettlemoyer, L. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 37

HTLM: Hyper-Text Pre-Training and Prompting of Language Models

Aghajanyan, A., Okhonko, D., Lewis, M., Joshi, M., Xu, H., Ghosh, G., & Zettlemoyer, L. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 35

Demystifying clip data

Xu, H., Xie, S., Tan, X. E., Huang, P.-Y., Howes, R., Sharma, V., Li, S.-W., Ghosh, G., Zettlemoyer, L., & Feichtenhofer, C. (2023) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 34

Continual Learning via Sparse Memory Finetuning

Lin, J., Zettlemoyer, L., Ghosh, G., Yih, W.-T., Markosyan, A., Berges, V.-P., & Oğuz, B. (2025) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 33

“A theme-rewriting approach for generating algebra word problems,”

Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 30

Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models

Blevins, T., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 27

RA-DIT: Retrieval-Augmented Dual Instruction Tuning

Lin, X. V., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., et al. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 26

Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems

Feng, S., Wang, Z., Goyal, P., Wang, Y., Shi, W., Xia, H., Palangi, H., Zettlemoyer, L., Tsvetkov, Y., Lee, C.-Y., et al. (2025) · arXiv

Topic ModelingMetaheuristic Optimization Algorithms Research

Cited by 18