Personalized Text Generation with Fine-Grained Linguistic Control

Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, Vipul Raheja

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.
Anthology ID:
2024.personalize-1.8
Volume:
Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
Month:
March
Year:
2024
Address:
St. Julians, Malta
Editors:
Ameet Deshpande, EunJeong Hwang, Vishvak Murahari, Joon Sung Park, Diyi Yang, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
Venues:
PERSONALIZE | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
88–101
Language:
URL:
https://aclanthology.org/2024.personalize-1.8
DOI:
Bibkey:
Cite (ACL):
Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, and Vipul Raheja. 2024. Personalized Text Generation with Fine-Grained Linguistic Control. In Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024), pages 88–101, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Personalized Text Generation with Fine-Grained Linguistic Control (Alhafni et al., PERSONALIZE-WS 2024)
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https://preview.aclanthology.org/naacl24-info/2024.personalize-1.8.pdf
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