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Home/e-Resources/Artificial Intelligence & Machine Learning/UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Conference ProceedingsArtificial Intelligence & Machine Learning

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

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Abstract & Overview

Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih, Madian Khabsa. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Cite this record

Yuning Mao, Lambert Mathias, Rui Lian Hou, Amjad Almahairi, Hao Ma, Jiawei Han, et al. (2022). UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning. Annual Meeting of the Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.433

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Mao, Y., Mathias, L., Hou, R. L., Almahairi, A., Ma, H., Han, J., Yih, S., & Khabsa, M. (2022). UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning. Annual Meeting of the Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.433
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