TL;DR
This paper introduces ZeroStylus, a hierarchical framework for long-text style transfer using LLMs, which enhances style consistency and structural coherence by combining sentence and paragraph-level transformations without needing parallel data.
Contribution
The paper presents a novel hierarchical approach that leverages template acquisition and guided generation for effective long-text style transfer with LLMs, outperforming baseline methods.
Findings
Achieves higher style consistency and content preservation scores.
Demonstrates the importance of paragraph-level structural encoding.
Enables coherent long-text style transfer without parallel corpora or fine-tuning.
Abstract
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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
This paper presents long-text style transfer using LLMs in a zero-shot setting. The proposed framework introduces a dual-layered hierarchical design that separates sentence-level stylistic adaptation from paragraph-level structural coherence, non-trivial contribution beyond prior sentence-centric TST methods. The methodology is described with formal definitions, prompt examples, and theoretical analysis. Results demonstrate improvements in style consistency, content preservation, and expression
1. The evaluation setup relies on subjective or indirect measures of stylistic quality and could benefit from more transparent statistical analysis or error decomposition. 2.The baselines are reasonable but not exhaustive; comparisons with recent open-source document-level TST or retrieval-based rewriting systems would strengthen claims. 3. Implementation details such as template clustering thresholds, embedding models, and ablation granularity are insufficiently detailed for reproducibility.
1.The idea of introducing a hierarchical template-matching mechanism for zero-shot long-text style transfer is novel and differentiates the work from existing sentence-level approaches. 2.The proposed two-stage framework effectively addresses structural coherence in long-form text, showing quantitative improvements over standard LLM style-transfer baselines.
1.The framework is only evaluated on academic-style writing 2.The overall pipeline is complex and depends on multiple sequential prompt calls. This limits reproducibility and makes real-world deployment difficult. 3.The paper lacks full-document coherence evaluation, which is critical when assessing LLM rewriting accuracy in long-text settings.
- Clear hierarchical formulation with concrete phases, repositories, and matching criteria (incl. clustering and thresholding to control template growth). - Practical pipeline for long texts: dual matching (sentence + paragraph), refinement, and bounded-context rewriting specifically aimed at preventing mid-document style drop-off. - Evidence of benefit: StructuredRewritten (full pipeline) generally improves style consistency while maintaining better content preservation than sentence-only var
- Evaluation dependence on LLMs: The tri-axial score uses paragraph-embedding similarity and LLM-assisted judgments. The same class of LLMs (GPT-4o, DeepSeek-R1) is also used for extraction and rewriting, raising bias and leakage concerns. Stronger human-only evaluation or cross-model evaluators would help. Moreover, existing style transfer evaluation frameworks [1] are not used. - Baselines comparisons omit strong long-form controls (e.g., hierarchical planning/prompting, retrieval-guided auth
1. These days users want precise control over how their LLM chatbots sound stylistically (CharacterAI, Gemini Gems etc.), which makes long-form style transfer an important and timely research problem. 2. The paper uses a variety of evaluation strategies to validate their method: (1) a joint automatic-human pointwise evaluation including human experts; (2) an automatic pairwise comparison between close systems to get finer-grained judgements. On these evaluations, the authors show consistent imp
1. **The paper is severly lacking in qualitative examples, which makes contributions very unclear**: The authors should be more precise about the kinds of style transfer tasks they are tackling, and give qualitative examples for why a paragraph-level representation is essential. Right now there is just 1 qualitative example in the Appendix, and it leaves the problem statement quite unclear to me: ``` Style Input Document: ”Bayesian optimization achieves 92% accuracy. This outperforms random sea
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