Zettlemoyer, Luke

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv

Digital Humanities and ScholarshipComputational and Text Analysis Methods

Cited by 16,515

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 11,983

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 8,821

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8,816

Unsupervised Cross-lingual Representation Learning at Scale

Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 5,637

QLoRA: Efficient Finetuning of Quantized LLMs

Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 3,832

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 1,690

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Joshi, M., Choi, E., Weld, D. S., & Zettlemoyer, L. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,650

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023) · arXiv

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 1,150

End-to-end Neural Coreference Resolution

Lee, K., He, L., Lewis, M., & Zettlemoyer, L. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 869

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 755

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 752

QuAC: Question Answering in Context

Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSpeech and dialogue systems

Cited by 714

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 540

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 482

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Topic ModelingNatural Language Processing Techniques

Cited by 482

Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Ghazvininejad, M., Levy, O., Liu, Y., & Zettlemoyer, L. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 464

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 434

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 407

Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

Clark, C., Yatskar, M., & Zettlemoyer, L. (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 381

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Aghajanyan, A., Zettlemoyer, L., & Gupta, S. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 374

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

Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022) · arXiv

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 305