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} }