Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models.

Alireza Salemi|Hamed Zamani


Anthology ID:DBLP:conf/ictir/SalemiZ25a
Volume:Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025
Year:2025
Venue:International Conference on the Theory of Information Retrieval (ICTIR)
Publisher:ACM
Pages:286-296
URL:https://doi.org/10.1145/3731120.3744595
DOI:https://doi.org/10.1145/3731120.3744595
DBLP:conf/ictir/SalemiZ25a
BibTeX:
@inproceedings{salemi-2025-comparing, author = {Alireza Salemi and Hamed Zamani}, editor = {Hamed Zamani and Laura Dietz and Benjamin Piwowarski and Sebastian Bruch}, title = {{Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models}}, booktitle = {{Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025}}, pages = {286--296}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3731120.3744595}, doi = {https://doi.org/10.1145/3731120.3744595} }