@inproceedings{alhafni-etal-2024-personalized,
title = "Personalized Text Generation with Fine-Grained Linguistic Control",
author = "Alhafni, Bashar and
Kulkarni, Vivek and
Kumar, Dhruv and
Raheja, Vipul",
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-1.8",
pages = "88--101",
abstract = "As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that reflect authors{'} writing styles, such as formality, domain, or sentiment. In this paper, we focus on controlling fine-grained attributes spanning multiple linguistic dimensions, such as lexical and syntactic attributes. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text based on multiple fine-grained linguistic attributes. We systematically investigate the performance of various large language models on our benchmark and draw insights from the factors that impact their performance. We make our code, data, models, and benchmarks publicly available.",
}
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%0 Conference Proceedings
%T Personalized Text Generation with Fine-Grained Linguistic Control
%A Alhafni, Bashar
%A Kulkarni, Vivek
%A Kumar, Dhruv
%A Raheja, Vipul
%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 alhafni-etal-2024-personalized
%X As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that reflect authors’ writing styles, such as formality, domain, or sentiment. In this paper, we focus on controlling fine-grained attributes spanning multiple linguistic dimensions, such as lexical and syntactic attributes. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text based on multiple fine-grained linguistic attributes. We systematically investigate the performance of various large language models on our benchmark and draw insights from the factors that impact their performance. We make our code, data, models, and benchmarks publicly available.
%U https://aclanthology.org/2024.personalize-1.8
%P 88-101
Markdown (Informal)
[Personalized Text Generation with Fine-Grained Linguistic Control](https://aclanthology.org/2024.personalize-1.8) (Alhafni et al., PERSONALIZE-WS 2024)
ACL