Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce.
Jingying Zeng|Zhenwei Dai|Hui Liu|Samarth Varshney|Zhiji Liu|Chen Luo|Zhen Li|Qi He|Xianfeng Tang
| Anthology ID: | DBLP:conf/sigir/ZengDLVLLLHT25 |
|---|---|
| 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: | 4244-4248 |
| URL: | https://doi.org/10.1145/3726302.3731941 |
| DOI: | https://doi.org/10.1145/3726302.3731941 |
| DBLP: | conf/sigir/ZengDLVLLLHT25 |
| BibTeX: |
@inproceedings{zeng-2025-examples,
author = {Jingying Zeng and
Zhenwei Dai and
Hui Liu and
Samarth Varshney and
Zhiji Liu and
Chen Luo and
Zhen Li and
Qi He and
Xianfeng Tang},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce}},
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 = {4244--4248},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3731941},
doi = {https://doi.org/10.1145/3726302.3731941}
}