Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters.
Yukang Xie|Chengyu Wang|Junbing Yan|Jiyong Zhou|Feiqi Deng|Jun Huang
| Anthology ID: | DBLP:conf/wsdm/Xie0YZDH24 |
|---|---|
| Volume: | Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024 |
| Year: | 2024 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 1094-1097 |
| URL: | https://doi.org/10.1145/3616855.3635690 |
| DOI: | https://doi.org/10.1145/3616855.3635690 |
| DBLP: | conf/wsdm/Xie0YZDH24 |
| BibTeX: |
@inproceedings{xie-2024-making,
author = {Yukang Xie and
Chengyu Wang and
Junbing Yan and
Jiyong Zhou and
Feiqi Deng and
Jun Huang},
editor = {Luz Angelica Caudillo-Mata and
Silvio Lattanzi and
Andr\'{e}s Mu\~{n}oz Medina and
Leman Akoglu and
Aristides Gionis and
Sergei Vassilvitskii},
title = {{Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters}},
booktitle = {{Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024}},
pages = {1094--1097},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3616855.3635690},
doi = {https://doi.org/10.1145/3616855.3635690}
}