The DOI matches its Crossref record; 82 of the 89 references it cites were found in a registry or an index.
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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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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Deep Contextualized Word Representations
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Overcoming catastrophic forgetting in neural networks
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Cited by
Sorted by citations
Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., & Lim, S.-N. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19827-4_41 · Cited by 1,965
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys · doi:10.1145/3605943 · Cited by 1,387
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
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Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 1,245
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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