@inproceedings{neelakanteswara-etal-2024-rags,
title = "{RAG}s to Style: Personalizing {LLM}s with Style Embeddings",
author = "Neelakanteswara, Abhiman and
Chaudhari, Shreyas and
Zamani, Hamed",
editor = "Deshpande, Ameet and
Hwang, EunJeong and
Murahari, Vishvak and
Park, Joon Sung and
Yang, Diyi and
Sabharwal, Ashish and
Narasimhan, Karthik and
Kalyan, Ashwin",
booktitle = "Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.personalize-1.11",
pages = "119--123",
abstract = "This paper studies the use of style embeddings to enhance author profiling for the goal of personalization of Large Language Models (LLMs). Using a style-based Retrieval-Augmented Generation (RAG) approach, we meticulously study the efficacy of style embeddings in capturing distinctive authorial nuances. The proposed method leverages this acquired knowledge to enhance the personalization capabilities of LLMs. In the assessment of this approach, we have employed the LaMP benchmark, specifically tailored for evaluating language models across diverse dimensions of personalization. The empirical observations from our investigation reveal that, in comparison to term matching or context matching, style proves to be marginally superior in the development of personalized LLMs.",
}
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%0 Conference Proceedings
%T RAGs to Style: Personalizing LLMs with Style Embeddings
%A Neelakanteswara, Abhiman
%A Chaudhari, Shreyas
%A Zamani, Hamed
%Y Deshpande, Ameet
%Y Hwang, EunJeong
%Y Murahari, Vishvak
%Y Park, Joon Sung
%Y Yang, Diyi
%Y Sabharwal, Ashish
%Y Narasimhan, Karthik
%Y Kalyan, Ashwin
%S Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F neelakanteswara-etal-2024-rags
%X This paper studies the use of style embeddings to enhance author profiling for the goal of personalization of Large Language Models (LLMs). Using a style-based Retrieval-Augmented Generation (RAG) approach, we meticulously study the efficacy of style embeddings in capturing distinctive authorial nuances. The proposed method leverages this acquired knowledge to enhance the personalization capabilities of LLMs. In the assessment of this approach, we have employed the LaMP benchmark, specifically tailored for evaluating language models across diverse dimensions of personalization. The empirical observations from our investigation reveal that, in comparison to term matching or context matching, style proves to be marginally superior in the development of personalized LLMs.
%U https://aclanthology.org/2024.personalize-1.11
%P 119-123
Markdown (Informal)
[RAGs to Style: Personalizing LLMs with Style Embeddings](https://aclanthology.org/2024.personalize-1.11) (Neelakanteswara et al., PERSONALIZE-WS 2024)
ACL
- Abhiman Neelakanteswara, Shreyas Chaudhari, and Hamed Zamani. 2024. RAGs to Style: Personalizing LLMs with Style Embeddings. In Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024), pages 119–123, St. Julians, Malta. Association for Computational Linguistics.