Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models

Yuyan Chen, Qiang Fu, Ge Fan, Lun Du, Jian-Guang Lou, Shi Han, Dongmei Zhang 0001, Zhixu Li, Yanghua Xiao. Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models. In Ingo Frommholz, Frank Hopfgartner, Mark Lee 0001, Michael Oakes 0001, Mounia Lalmas, Min Zhang 0006, Rodrygo L. T. Santos, editors, Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023. pages 276-285, ACM, 2023. [doi]

@inproceedings{ChenFFDLH0LX23,
  title = {Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models},
  author = {Yuyan Chen and Qiang Fu and Ge Fan and Lun Du and Jian-Guang Lou and Shi Han and Dongmei Zhang 0001 and Zhixu Li and Yanghua Xiao},
  year = {2023},
  doi = {10.1145/3583780.3614904},
  url = {https://doi.org/10.1145/3583780.3614904},
  researchr = {https://researchr.org/publication/ChenFFDLH0LX23},
  cites = {0},
  citedby = {0},
  pages = {276-285},
  booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023},
  editor = {Ingo Frommholz and Frank Hopfgartner and Mark Lee 0001 and Michael Oakes 0001 and Mounia Lalmas and Min Zhang 0006 and Rodrygo L. T. Santos},
  publisher = {ACM},
}