用思维链规划让小模型精准还原角色说话风格
Structured Style-Rewrite with Chain-of-Thought Planning for Low-Resource Character Dialogue
- 将角色风格拆解为格式、句法、语用三维度，结合思维链规划
- 在小模型上实现0.632的风格得分，语义保真度达0.878
- 适合资源受限场景下角色对话生成，尤其适配消费级硬件
由于数据稀缺和角色风格难以分离，将小型语言模型（SLMs）应用于中文角色驱动生成仍具挑战。标准监督微调常捕捉表层语义，导致频繁出现角色不符（OOC）输出。本文将其视为受控的句级风格重写任务，将风格质量与对话上下文管理分离。提出结构化风格重写框架，将角色风格分解为可解释的格式签名、句法和语用维度，并结合思维链（CoT）监督进行显式风格规划。通过共享的直接偏好优化（CoT-Shared DPO）阶段，确保偏好学习聚焦于输出层面的风格执行而非推理轨迹差异。在四个不同来源领域的八位角色上实验表明，该方法使Qwen3-1.7B模型在保持强语义保真度（0.878）的同时，获得0.632的合理风格得分，位于所有评估系统中的帕累托前沿，且显著优于更大规模基线（如GLM-4.7），可在消费级硬件上运行。
原文摘要 · Abstract (English)
Applying Small Language Models (SLMs) to Chinese character-driven generation remains challenging due to data scarcity and the difficulty of disentangling character style. Standard Supervised Fine-Tuning (SFT) often captures surface-level semantics but produces frequent Out-Of-Character (OOC) outputs. We frame this as a controlled sentence-level style rewriting task, which isolates stylistic quality from dialogue context management. We propose a Structured Style-Rewrite Framework that decomposes character style into interpretable format signature, syntactic, and pragmatic dimensions, combined with Chain-of-Thought (CoT) supervision for explicit style planning. A CoT-Shared Direct Preference Optimization (DPO) stage further aligns style planning with surface realization by ensuring preference learning targets output-level style execution rather than reasoning trace differences. Experiments across eight characters from four diverse source domains demonstrate that our method enables a Qwen3-1.7B model to achieve a Valid Style Score of $0.632$ while maintaining strong semantic fidelity (0.878), placing on the Pareto frontier among the evaluated systems and outperforming significantly larger baselines (e.g., GLM-4.7) on consumer hardware.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。