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} }