Learning Text Styles: A Study on Transfer, Attribution, and Verification
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909699105882112 |
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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 |