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Text Style Transfer: An Introductory Overview

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arxiv 2407.14822 v1 pith:YBBW3TS2 submitted 2024-07-20 cs.CL

Sourabrata Mukherjee , Ondrej Dušek This is my paper · ORCID
classification cs.CL
keywords textstyleoverviewtransferattributesintroductorylanguagewidely
verification ladder T0 review T1 audit T2 compute T3 formal

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Text Style Transfer (TST) is a pivotal task in natural language generation to manipulate text style attributes while preserving style-independent content. The attributes targeted in TST can vary widely, including politeness, authorship, mitigation of offensive language, modification of feelings, and adjustment of text formality. TST has become a widely researched topic with substantial advancements in recent years. This paper provides an introductory overview of TST, addressing its challenges, existing approaches, datasets, evaluation measures, subtasks, and applications. This fundamental overview improves understanding of the background and fundamentals of text style transfer.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLMs can be easily Confused by Instructional Distractions

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A new benchmark, DIM-Bench, shows that LLMs frequently follow instructions hidden inside the target input rather than the user's actual instruction, even when explicitly told to ignore them.

  2. Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A dual-layered sentence and paragraph template method for zero-shot long-text style transfer, with a reported average gain of 0.20 over direct prompting but limited statistical and external support.

  3. StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A LoRA adapter trained on unstructured text and merged into an instruct model transfers style with a modest instruction-following loss, outperforming few-shot prompting on content fidelity.

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