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

Yusen Wu, Xiaotie Deng

arXiv:2505.07888v1cs.CLcs.AI

TL;DR

Long-text style transfer must handle style drift and preserve paragraph-level coherence, while current zero-shot methods largely focus on sentence-level conversion. ZeroStylus combines sentence and paragraph templates in a hierarchical rewriting framework; the paper reports improved content preservation and style consistency relative to baselines.

  • Problem

    Long-text style transfer must preserve inter-sentence structure and macro-stylistic features, but current zero-shot approaches predominantly focus on sentence-level conversion.

    Source: Introduction · p. 2

  • Method

    ZeroStylus extracts reusable sentence and paragraph templates from reference texts and uses their hierarchical matching to guide LLM rewriting.

    Source: Introduction · p. 2

  • Results

    ZeroStylus achieves better content preservation and style consistency than baselines while maintaining relative overall quality metrics.

    Source: Conclusion · p. 7

  • Takeaways & Limitations

    The framework’s explicit rhetorical-structure encoding and dynamic template repositories mitigate style drift in extended text generation.

    Source: Conclusion · p. 7

    The cited few-shot conversation-transfer approach relies on manually constructed parallel conversations, which may be impractical for large-scale style domains.

    Source: Conversation Style Transfer using… · p. 4

Abstract

from arXiv · show

This paper addresses the challenge in long-text style transfer using zero-shot learning of large language models (LLMs), proposing a hierarchical framework that combines sentence-level stylistic adaptation with paragraph-level structural coherence. We argue that in the process of effective paragraph-style transfer, to preserve the consistency of original syntactic and semantic information, it is essential to perform style transfer not only at the sentence level but also to incorporate paragraph-level semantic considerations, while ensuring structural coherence across inter-sentential relationships. Our proposed framework, ZeroStylus, operates through two systematic phases: hierarchical template acquisition from reference texts and template-guided generation with multi-granular matching. The framework dynamically constructs sentence and paragraph template repositories, enabling context-aware transformations while preserving inter-sentence logical relationships. Experimental evaluations demonstrate significant improvements over baseline methods, with structured rewriting achieving 6.90 average score compared to 6.70 for direct prompting approaches in tri-axial metrics assessing style consistency, content preservation, and expression quality. Ablation studies validate the necessity of both template hierarchies during style transfer, showing higher content preservation win rate against sentence-only approaches through paragraph-level structural encoding, as well as direct prompting method through sentence-level pattern extraction and matching. The results establish new capabilities for coherent long-text style transfer without requiring parallel corpora or LLM fine-tuning.

Introduction

Long-text style transfer must preserve both local stylistic features and paragraph-level coherence, yet existing methods and evaluations often miss these structural demands. ZeroStylus addresses this with sentence and paragraph templates, decoupled mappings, and length-constrained rewriting.

Introduction

  • Existing zero-shot LLM methods largely process short texts, while fine-tuning methods can require domain-specific parallel data and substantial computation.Long inputs may receive only partial style adaptation, and segmentation risks losing inter-sentence coherence.Source: Introduction · pp. 1–2
  • Long-text transfer faces style drift, selective paragraph modification, and evaluation metrics that miss inter-sentence logic and macro-stylistic features.Traditional approaches often treat style as local features, overlooking paragraph development and argumentation patterns.Source: Introduction · p. 2
  • ZeroStylus extracts reusable sentence templates from reference paragraphs, maps them to paragraph patterns, and matches both levels during sequential LLM rewriting.Paragraph templates are selected to align with aggregated sentence patterns while preserving inter-sentence coherence.Source: Introduction · p. 2
  • Decoupling sentence and paragraph template mappings enables selective adaptation from reference subsets, including temporal-specific paragraph templates, without reprocessing entire corpora.Length-constrained iterative rewriting processes bounded segments to mitigate degeneration and premature termination.Source: Introduction · p. 2
  • Experiments report stronger style retention than baseline methods, and ablations support the use of both hierarchical template matching and length-constrained generation.The framework targets paragraph-structure preservation through template-guided sequences and micro-stylistic consistency through sentence-template alignment.Source: Introduction · p. 2

Related Work

Prior style-transfer research progressed from sentence-level modeling to LLM-based approaches, but long-text coherence and macro-stylistic evaluation remain unresolved. Zero-shot and inference-time methods reduce training demands, yet reported style degradation persists in long texts.

Traditional Style Transfer

  • Traditional methods span sentence-level disentanglement, contrastive learning, and adversarial latent representations, but have struggled with paragraph coherence and explicit linguistic or cultural features.The cited approaches include StyleLM-like preservation challenges and paragraph-level fragmentation.Source: Traditional Style Transfer · p. 2

Style Transfer with LLMs

  • LLM-based transfer includes zero-shot prompting and document-level frameworks, but evaluation often relies on lexical metrics and some document methods require domain-specific parallel data.These approaches remain constrained in long-range consistency under unsupervised settings.Source: Style Transfer with LLMs · pp. 2–3
  • Current methods still struggle to preserve long-text coherence and evaluate author-specific argumentation or rhetorical preferences beyond micro-linguistic features.Reported problems include style drift across dialogue turns and selective paragraph modification.Source: Style Transfer with LLMs · p. 3

Zero-Shot Inference and Chain-of-Thought in LLMs

  • Few-shot and zero-shot inference-time techniques avoid additional training, while CoT trades inference-time computation for improved final-output accuracy in cited settings.Prefix tuning, distillation, and few-shot approaches have shown single-sentence gains but persistent long-text style degradation.Source: Zero-Shot Inference and… · p. 3

Preliminaries

The cited background covers author-specific rewriting and few-shot conversation transfer, highlighting their evaluation approaches and limits. It also describes ZeroStylus Phase 1 as building sentence- and paragraph-pattern sets from reference style text.

Adapting Language Models for Non-Parallel Author-Stylized Rewriting

  • StyleLM rewrites inputs into author-specific styles without parallel data by pretraining and fine-tuning a Transformer-based model with a cascaded encoder-decoder and denoising objective.Its evaluation measures lexical, syntactic, and surface style alignment plus content preservation, with style consistency assessed using MSE and JSD.Source: Adapting Language Models for… · p. 3
  • StyleLM performs best on short texts and simple stylistic features but struggles with long sentences and complex style transfers.The passage identifies architectural refinement and training optimization as needed for more complex structures.Source: Adapting Language Models for… · p. 3

Conversation Style Transfer using Few-Shot Learning

  • Few-shot conversation transfer first removes source style with in-context learning, then rewrites the style-free dialogue toward a target style using example dialogues.The approach addresses defining style attributes and parallel-data scarcity.Source: Conversation Style Transfer using… · p. 3
  • Human evaluations report that multi-turn context improves style matching, appropriateness, and semantic correctness relative to utterance- or sentence-level transfer.Transferring training-data styles to match test data also improves multi-domain intent-classification F1 scores.Source: Conversation Style Transfer using… · p. 3
  • Figure 1 describes Phase 1 as preprocessing style text to build sentence- and paragraph-pattern sets.Source: Figure 1 · p. 4
  • The conversation method relies on manually constructed style-to-style-free parallel conversations, which may be impractical for large-scale style domains.More context may also diminish style strength or produce semantically dissimilar responses.Source: Conversation Style Transfer using… · p. 4

Methods

ZeroStylus transfers long-text style through two coordinated phases: acquiring sentence- and paragraph-level templates, then using them to guide generation and coherence refinement. Its design combines academic writing’s rhetorical structures, section modules, and disciplinary conventions with source-content preservation.

Methods

  • Sentence templates cluster encoder representations with DBSCAN, while paragraph templates encode inter-sentence relationships and expand only when sufficiently distinct.The paragraph-template repository uses a distance threshold to limit proliferation while accommodating structural variation.Source: Phase 1: Hierarchical Template… · p. 4
  • Multi-granular matching retrieves a sentence template for each source sentence and a structural template for the source paragraph.The dual matching is intended to support local stylistic consistency and global coherence.Source: Phase 2: Template-Guided Generation · p. 5
  • Generation conditions each transformed sentence on its matched lexical pattern, paragraph-level structural constraints, and original semantic content.This conditioning is described as preserving content while adapting expression.Source: Phase 2: Template-Guided Generation · p. 5
  • A final paragraph-level refinement adjusts transitions, discourse markers, and referential consistency to align with the structural template.The paper frames academic style through rhetorical structures, section modules, and disciplinary conventions; modules contain logical structures such as literature-review or methodology descriptions.Source: Methods · p. 4, Phase 2: Template-Guided Generation · p. 5

Experiments

The experiments compare long-text style-transfer methods using style, content, and expression measures and pairwise adversarial evaluations. StructuredRewritten combines sentence-level rewriting with paragraph-level template matching, while the ablations report gains from sentence-pattern extraction and paragraph-level structural encoding.

Style Text and Transfer Pipeline

  • The evaluation averages results from GPT4-o and DeepSeek-R1, each used for style extraction and transformation.Reference material is sampled from one author’s articles, with the number of articles ranging from 1 to 5 and total reference length set to about three times the source length.Source: Style Text and Transfer Pipeline · p. 5

Benchmarking Style Transfer Quality from Different Methods

  • The benchmark uses 500 randomly selected academic-paper samples and combines normalized model-based scores with human ratings.The evaluation’s three axes are style consistency, content preservation, and expression quality; human judges score from 0 to 10.Source: Benchmarking Style Transfer Quality… · p. 5
  • DirectPrompt preserves semantics better but achieves the lowest stylization, while TemplateOnly and ConvTransfer show semantic or structural costs from sentence-level processing.DirectPrompt tends to modify initial paragraphs more than later ones; TemplateOnly imitates reference sentence patterns without inter-sentence relationships, and ConvTransfer loses contextual structure during destylization, partly mitigated by multi-sentence batches.Source: Benchmarking Style Transfer Quality… · p. 6
  • StructuredRewritten preserves TemplateOnly’s stylization strength while maintaining DirectPrompt’s paragraph-level semantic coherence.The method combines hierarchical paragraph-level template matching with sentence-level rewriting.Source: Benchmarking Style Transfer Quality… · p. 6

Adversarial Evaluation

  • 57.3% average win rate in style-transfer accuracy and 53.3% in semantic preservation favor SentencePattern over TemplateOnly.The authors attribute the gains to fewer template-mismatch errors and less leakage of non-stylistic reference details; human preference scores are comparable.Source: Adversarial Evaluation · p. 7
  • 57% versus 43% semantic-preservation win rate favors StructuredRewritten over SentencePattern, while stylization strength remains close at over 46% win rate.Expression-quality outcomes were around 50% across win-or-lose samples; the authors report paragraph-level encoding as better at preserving inter-sentence relationships than sentence-only processing.Source: Adversarial Evaluation · p. 7

Discussions

The discussion identifies remaining limitations and outlines directions for improving long-text style-transfer evaluation and extending the framework’s scope.

Discussions

  • Experiments show effectiveness against strict zero-shot baselines, but the authors identify several limitations and future-work directions.These include benchmarking, semantic splitting, task-specific evaluation, and extending hierarchical parsing.Source: Discussions · p. 7
  • Current benchmarks lack systematic quantitative evaluation for long-form articles, motivating dedicated metrics, while semantic splitting could improve unit-level rewriting and semantic capture.The authors also note that author-style assessments vary across domains and applications, calling for task-specific template extraction and evaluation.Source: Discussions · p. 7
  • The framework could add paragraph types, roles, and interparagraph relationships, with further structural design across documents or code files.Source: Discussions · p. 7

Conclusion

ZeroStylus uses hierarchical template matching for zero-shot long-text style transfer. The authors report improved content preservation and style consistency relative to baselines, and identify multilingual adaptation and efficient template updating as future work.

Conclusion

  • ZeroStylus decouples sentence-level pattern extraction from paragraph-level structural modeling through hierarchical template matching.Source: Conclusion · p. 7
  • The method achieves better content preservation and style consistency while maintaining relative overall quality metrics compared to baselines.Source: Conclusion · p. 7
  • Experimental validation across multiple paradigms and adversarial tests supports preservation of micro-stylistic features and macro-structural patterns, with preference over ablated variants.Source: Conclusion · p. 7
  • The authors identify multilingual adaptation and efficient template updating as future directions for broader applicability across stylistic domains.Source: Conclusion · p. 7

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