Learning Text Styles: A Study on Transfer, Attribution, and Verification

Fuente: arXiv
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Main Author: Hu, Zhiqiang
Format: Preprint
Published: 2025
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author Hu, Zhiqiang
author_facet Hu, Zhiqiang
contents This thesis advances the computational understanding and manipulation of text styles through three interconnected pillars: (1) Text Style Transfer (TST), which alters stylistic properties (e.g., sentiment, formality) while preserving content; (2)Authorship Attribution (AA), identifying the author of a text via stylistic fingerprints; and (3) Authorship Verification (AV), determining whether two texts share the same authorship. We address critical challenges in these areas by leveraging parameter-efficient adaptation of large language models (LLMs), contrastive disentanglement of stylistic features, and instruction-based fine-tuning for explainable verification.
format Preprint
id arxiv_https___arxiv_org_abs_2507_16530
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Learning Text Styles: A Study on Transfer, Attribution, and Verification
Hu, Zhiqiang
Computation and Language
This thesis advances the computational understanding and manipulation of text styles through three interconnected pillars: (1) Text Style Transfer (TST), which alters stylistic properties (e.g., sentiment, formality) while preserving content; (2)Authorship Attribution (AA), identifying the author of a text via stylistic fingerprints; and (3) Authorship Verification (AV), determining whether two texts share the same authorship. We address critical challenges in these areas by leveraging parameter-efficient adaptation of large language models (LLMs), contrastive disentanglement of stylistic features, and instruction-based fine-tuning for explainable verification.
title Learning Text Styles: A Study on Transfer, Attribution, and Verification
topic Computation and Language
url https://arxiv.org/abs/2507.16530