@inproceedings{sun-etal-2025-persona,
title = "Persona-{DB}: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement",
author = "Sun, Chenkai and
Yang, Ke and
Gangi Reddy, Revanth and
Fung, Yi and
Chan, Hou Pong and
Small, Kevin and
Zhai, ChengXiang and
Ji, Heng",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.20/",
pages = "281--296",
abstract = "The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has largely focused on enhancing the retrieval stage and devoted limited exploration toward optimizing the representation of the database, a crucial aspect for tasks such as personalization. In this work, we examine the problem from a novel angle, focusing on how data can be better represented for more data-efficient retrieval in the context of LLM customization. To tackle this challenge, we introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts and collaborative refinement to effectively bridge knowledge gaps among users. In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size, a critical advantage in scenarios with extensive histories or limited context windows. Our experiments also indicate a marked improvement of over 10{\%} under cold-start scenarios, when users have extremely sparse data. Furthermore, our analysis reveals the increasing importance of collaborative knowledge as the retrieval capacity expands."
}
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%0 Conference Proceedings
%T Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
%A Sun, Chenkai
%A Yang, Ke
%A Gangi Reddy, Revanth
%A Fung, Yi
%A Chan, Hou Pong
%A Small, Kevin
%A Zhai, ChengXiang
%A Ji, Heng
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F sun-etal-2025-persona
%X The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has largely focused on enhancing the retrieval stage and devoted limited exploration toward optimizing the representation of the database, a crucial aspect for tasks such as personalization. In this work, we examine the problem from a novel angle, focusing on how data can be better represented for more data-efficient retrieval in the context of LLM customization. To tackle this challenge, we introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts and collaborative refinement to effectively bridge knowledge gaps among users. In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size, a critical advantage in scenarios with extensive histories or limited context windows. Our experiments also indicate a marked improvement of over 10% under cold-start scenarios, when users have extremely sparse data. Furthermore, our analysis reveals the increasing importance of collaborative knowledge as the retrieval capacity expands.
%U https://aclanthology.org/2025.coling-main.20/
%P 281-296
Markdown (Informal)
[Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement](https://aclanthology.org/2025.coling-main.20/) (Sun et al., COLING 2025)
ACL
- Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, and Heng Ji. 2025. Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement. In Proceedings of the 31st International Conference on Computational Linguistics, pages 281–296, Abu Dhabi, UAE. Association for Computational Linguistics.