AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
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Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.
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The rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge.
“Thus, the rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge [328].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [328]
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References 45 · Cited by 77
Research works printed but not linked: 6
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems, volume 33, pages 1877-1901.
- PMLR.
- Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. In In- ternational Conference on Machine Learning, pages 2790-2799. PMLR.
- Michael Matena and Colin Raffel. 2021. Merging mod- els with fisher-weighted averaging. arXiv preprint arXiv:2111.09832. · arXiv
- Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 2017. The e2e dataset: New challenges for end-to- end generation. arXiv preprint arXiv:1706.09254. · arXiv
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.
- Authors
- Wang, Y., Agarwal, S., Mukherjee, S., Liu, X., Gao, J., Awadallah, A. H., & Gao, J.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 5744-5760
- OpenAlex
- W4385572883
- Cited by
- 77
- References
- 45
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 28 · 2023: 2
- FWCI
- 8.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2141037, 1747614)
- 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 fine-tuning, model adaptation
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