A T5-based few-shot style transfer system that selects a context-dependent style coordinate from references and realizes it with hypernetwork-generated adapters outperforms prior baselines including LLMs on Reddit, Blog, and News.
T iny S tyler: Efficient Few‑Shot Text Style Transfer with Authorship Embeddings
4 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.
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.
citing papers explorer
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HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks
A T5-based few-shot style transfer system that selects a context-dependent style coordinate from references and realizes it with hypernetwork-generated adapters outperforms prior baselines including LLMs on Reddit, Blog, and News.
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Interpreting Style Representations via Style-Eliciting Prompts
A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.
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Measuring Embedding Sensitivity to Authorial Style in French: Comparing Literary Texts with Language Model Rewritings
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.
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StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer
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.