Artetxe, Mikel

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

Unsupervised Neural Machine Translation

Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 632

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

Improving Language Plasticity via Pretraining with Active Forgetting

Chen, Y., Marchisio, K., Raileanu, R., Adelani, D., Stenetorp, P. L. E. S., Riedel, S., & Artetxe, M. (2023) · Advances in Neural Information Processing Systems 36

Domain Adaptation and Few-Shot LearningNatural Language Processing Techniques

Cited by 3

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models

Padlewski, P., Bain, M., Henderson, M., Zhu, Z., Relan, N., Pham, H., Ong, D., Aleksiev, K., Ormazabal, A., Phua, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1