Large Models are Good Annotators for Zero-Shot Learning.
Qingzhi He|Yizhen Jia|Wentong Li|Shengcai Liao|Rong Quan|Tong Cui|Jie Qin
| Anthology ID: | DBLP:conf/sigir/HeJ0LQCQ25 |
|---|---|
| Volume: | Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025 |
| Year: | 2025 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 2764-2768 |
| URL: | https://doi.org/10.1145/3726302.3730219 |
| DOI: | https://doi.org/10.1145/3726302.3730219 |
| DBLP: | conf/sigir/HeJ0LQCQ25 |
| BibTeX: |
@inproceedings{he-2025-large,
author = {Qingzhi He and
Yizhen Jia and
Wentong Li and
Shengcai Liao and
Rong Quan and
Tong Cui and
Jie Qin},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{Large Models are Good Annotators for Zero-Shot Learning}},
booktitle = {{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025}},
pages = {2764--2768},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3730219},
doi = {https://doi.org/10.1145/3726302.3730219}
}