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AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing

arXiv:2603.23069v1 Announce Type: new Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora to model all target styles at once: this high-cost approach offers limited flexibility for target-specific adaptation, and often sacrifices meaning preservation for style transfer. In this paper, we propose AuthorMix: a lightweight, modular, and interpretable style transfer framework. We train individual, style-specific LoRA adapters on a small set of high-resource authors, allowing the rapid training of specialized adaptation models for each new target via learned, layer-wise adapter mixing, using only a handful of target style training examples. AuthorMix outperforms existing, SoTA style-transfer baselines -- as well as GPT-5.1 -- for low-resource targets, achieving the highest overall score and substantially improving meani

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Sarubi Thillainathan, Ji-Ung Lee, Michael Sullivan, Alexander Koller · · 1 min read · 2 views
#cs.CL #cs.AI

arXiv:2603.23069v1 Announce Type: new Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora to model all target styles at once: this high-cost approach offers limited flexibility for target-specific adaptation, and often sacrifices meaning preservation for style transfer. In this paper, we propose AuthorMix: a lightweight, modular, and interpretable style transfer framework. We train individual, style-specific LoRA adapters on a small set of high-resource authors, allowing the rapid training of specialized adaptation models for each new target via learned, layer-wise adapter mixing, using only a handful of target style training examples. AuthorMix outperforms existing, SoTA style-transfer baselines -- as well as GPT-5.1 -- for low-resource targets, achieving the highest overall score and substantially improving meaning preservation.

Executive Summary

The paper 'AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing' introduces a novel framework for authorship style transfer, addressing key limitations in existing methods. Unlike conventional approaches that rely on monolithic models trained on large corpora to handle multiple styles, AuthorMix employs a modular architecture where individual LoRA adapters are trained for high-resource authors. These adapters are then dynamically combined via layer-wise mixing, enabling rapid adaptation to new target authors with minimal training data. The method demonstrates superior performance in low-resource scenarios, outperforming state-of-the-art baselines and even advanced language models like GPT-5.1, while significantly improving meaning preservation. This work contributes to the field by offering a flexible, interpretable, and efficient solution for authorship style transfer, particularly in resource-constrained settings.

Key Points

  • ▸ Modular and lightweight architecture: AuthorMix trains individual LoRA adapters for specific authors, enabling rapid and scalable adaptation to new targets.
  • ▸ Layer-wise adapter mixing: The framework dynamically combines adapters at different layers, allowing for nuanced and interpretable style transfer without extensive retraining.
  • ▸ Superior performance in low-resource settings: AuthorMix outperforms existing state-of-the-art methods and large language models, particularly in scenarios with limited target data, while preserving the original meaning more effectively.

Merits

Innovative Modular Design

The use of individual LoRA adapters trained on high-resource authors, combined with layer-wise mixing, represents a significant departure from monolithic models. This modular approach not only reduces computational costs but also enhances flexibility and adaptability for new target authors.

Efficiency in Low-Resource Scenarios

AuthorMix demonstrates exceptional performance in low-resource settings, where traditional methods struggle due to insufficient training data. The framework’s ability to rapidly adapt to new authors with minimal examples sets it apart from existing approaches.

Preservation of Meaning

Unlike many style transfer methods that sacrifice semantic fidelity for stylistic accuracy, AuthorMix achieves a balance by leveraging layer-wise mixing to retain the original meaning while effectively transferring the target author’s style.

Interpretability

The layer-wise mixing mechanism provides a transparent and interpretable framework, allowing researchers and practitioners to understand how stylistic elements are transferred across different layers of the model.

Demerits

Dependency on High-Resource Authors

The framework’s performance relies heavily on the availability of well-trained adapters for high-resource authors. In scenarios where such adapters are unavailable or poorly trained, the system’s effectiveness may be compromised.

Generalization Challenges

While AuthorMix excels in low-resource settings, its ability to generalize to entirely unseen or highly diverse authorial styles remains untested. The framework’s adaptability to completely novel styles may require further validation.

Computational Overhead of Adapter Training

Although modular, the initial training of LoRA adapters for high-resource authors still incurs computational costs. For organizations with limited resources, this upfront investment may pose a barrier to adoption.

Expert Commentary

AuthorMix represents a significant advancement in the field of authorship style transfer, addressing critical gaps in existing methodologies. The modular and interpretable design of the framework not only enhances performance but also provides a pathway for scalable and ethical applications. The layer-wise adapter mixing mechanism is particularly noteworthy, as it allows for nuanced control over stylistic transfer while preserving semantic integrity—a challenge that has long plagued style transfer tasks. The paper’s empirical results, demonstrating superiority over state-of-the-art baselines and even advanced language models like GPT-5.1, underscore the practical utility of this approach. However, the framework’s reliance on high-resource adapters and its untested generalization to highly novel styles warrant further investigation. As NLP systems increasingly interact with human language, tools like AuthorMix that balance efficiency, interpretability, and performance will be indispensable. This work also raises important ethical considerations, particularly in the context of digital identity and misinformation, where stylistic mimicry could be weaponized. The authors’ focus on transparency and modularity provides a robust foundation for addressing these challenges, making AuthorMix a timely and impactful contribution to the field.

Recommendations

  • ✓ Further research should explore the generalization capabilities of AuthorMix to entirely unseen or highly diverse authorial styles, potentially through meta-learning techniques that enhance adapter adaptability.
  • ✓ Develop benchmarks and ethical guidelines for the deployment of authorship style transfer systems, particularly in high-stakes applications such as journalism or legal document generation, to mitigate risks of misuse.
  • ✓ Investigate the integration of AuthorMix with other NLP tasks, such as machine translation or summarization, to evaluate its versatility and potential for cross-domain applications.
  • ✓ Expand the framework to include multilingual authorship style transfer, addressing the growing demand for culturally and linguistically diverse stylistic adaptation.
  • ✓ Collaborate with policymakers to establish regulatory frameworks that promote the responsible use of style transfer technologies, ensuring alignment with public interest and ethical standards.

Sources

Original: arXiv - cs.CL

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