Paper
Implicit Style Conditioning: A Structured Style-Rewrite Framework for Low-Resource Character Modeling
Authors
Chanhui Zhu
Abstract
Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing (RP); however, small Language Models (SLMs) with highly stylized personas remains a challenge due to data scarcity and the complexity of style disentanglement. Standard Supervised Fine-Tuning (SFT) often captures surface-level semantics while failing to reproduce the intricate syntactic and pragmatic nuances of a character, leading to "Out-Of-Character" (OOC) generation. To address this, we propose a Structured Style-Rewrite Framework that explicitly disentangles style into three interpretable dimensions: lexical signatures (via PMI), syntactic patterns (grounded in PCFG rules), and pragmatic style. Furthermore, we introduce an implicit style conditioning strategy via Chain-of-Thought (CoT) distillation. By leveraging explicit reasoning traces during training as a strong inductive bias, our approach aligns the model's latent representations with structured style features, enabling high-fidelity stylized generation without requiring explicit reasoning tokens during inference. Extensive experiments on a specific high-stylization domain (anime characters) demonstrate that our method enables a Qwen-1.7B model to outperform significantly larger baselines (e.g., 4B Vanilla SFT) in style consistency and semantic fidelity. Our approach offers a data-efficient paradigm for democratizing inference and deployment on consumer hardware.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.05933v1</id>\n <title>Implicit Style Conditioning: A Structured Style-Rewrite Framework for Low-Resource Character Modeling</title>\n <updated>2026-03-06T06:04:47Z</updated>\n <link href='https://arxiv.org/abs/2603.05933v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.05933v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing (RP); however, small Language Models (SLMs) with highly stylized personas remains a challenge due to data scarcity and the complexity of style disentanglement. Standard Supervised Fine-Tuning (SFT) often captures surface-level semantics while failing to reproduce the intricate syntactic and pragmatic nuances of a character, leading to \"Out-Of-Character\" (OOC) generation. To address this, we propose a Structured Style-Rewrite Framework that explicitly disentangles style into three interpretable dimensions: lexical signatures (via PMI), syntactic patterns (grounded in PCFG rules), and pragmatic style. Furthermore, we introduce an implicit style conditioning strategy via Chain-of-Thought (CoT) distillation. By leveraging explicit reasoning traces during training as a strong inductive bias, our approach aligns the model's latent representations with structured style features, enabling high-fidelity stylized generation without requiring explicit reasoning tokens during inference. Extensive experiments on a specific high-stylization domain (anime characters) demonstrate that our method enables a Qwen-1.7B model to outperform significantly larger baselines (e.g., 4B Vanilla SFT) in style consistency and semantic fidelity. Our approach offers a data-efficient paradigm for democratizing inference and deployment on consumer hardware.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-06T06:04:47Z</published>\n <arxiv:comment>26 pages, 4 figures. Preprint</arxiv:comment>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Chanhui Zhu</name>\n </author>\n </entry>"
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