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Parameter-Efficient Transfer Learning with Diff Pruning
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Demi Guo, Alexander Rush, Yoon Kim. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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“These approaches cannot provide the inference-time adaptation needed for agents operating in real-time, often on resource-constrained hardware or without access to training infrastructure (Guo et al., 2021; Dettmers et al., 2023; Liu et al., 2024; Frantar & Alistarh, 2023).”
From Continual Learning, Not Training: Online Adaptation For Agents (2025) · cites this paper as Guo et al., 2021
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Metadata
- Authors
- Guo, D., Rush, A., & Kim, Y.
- Affiliations
- Harvard University (US)
- Cornell University (US)
- IBM (United States) (US)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 4884-4896
- OpenAlex
- W3174702398
- Cited by
- 220
- References
- 89
- Citations by year
- 2026: 22 · 2025: 42 · 2024: 51 · 2023: 63 · 2022: 35 · 2021: 7
- FWCI
- 18.02
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Funders
- National Science Foundation (1704834, 2037519)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- parameter-efficient transfer learning, diffusion models
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