A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models.
Shengyao Zhuang|Honglei Zhuang|Bevan Koopman|Guido Zuccon
| Anthology ID: | DBLP:conf/sigir/ZhuangZKZ24 |
|---|---|
| Volume: | Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 |
| Year: | 2024 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 38-47 |
| URL: | https://doi.org/10.1145/3626772.3657813 |
| DOI: | https://doi.org/10.1145/3626772.3657813 |
| DBLP: | conf/sigir/ZhuangZKZ24 |
| BibTeX: |
@inproceedings{zhuang-2024-setwise,
author = {Shengyao Zhuang and
Honglei Zhuang and
Bevan Koopman and
Guido Zuccon},
editor = {Grace Hui Yang and
Hongning Wang and
Sam Han and
Claudia Hauff and
Guido Zuccon and
Yi Zhang},
title = {{A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models}},
booktitle = {{Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024}},
pages = {38--47},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3626772.3657813},
doi = {https://doi.org/10.1145/3626772.3657813}
}