用多模型协作让普通人写的金融投诉更像专业法律文书。
Refining Financial Consumer Complaints through Multi-Scale Model Interaction
- 用轻量级分类器评估LLM输出，迭代优化文本
- 在自建金融投诉数据集上显著提升法律说服力
- 适合法律写作辅助与通用文本润色场景
法律文书要求清晰、正式且领域精准，但非专业人士撰写的文件常缺乏这些特征。本文探索法律文本精炼任务，将非正式的口语化输入转化为有说服力的法律论点。我们构建了中文金融纠纷记录数据集FinDR，包含官方判决对诉求合理性的标注。提出多尺度模型交互（MSMI）方法，利用轻量级分类器评估生成结果，并指导大语言模型进行迭代优化。实验表明，MSMI显著优于单次提示策略。此外，在多个短文本基准上验证了MSMI的泛化能力，表现出更强的对抗鲁棒性。研究揭示了多模型协作在法律文档生成及更广泛文本精炼任务中的潜力。
原文摘要 · Abstract (English)
Legal writing demands clarity, formality, and domain-specific precision-qualities often lacking in documents authored by individuals without legal training. To bridge this gap, this paper explores the task of legal text refinement that transforms informal, conversational inputs into persuasive legal arguments. We introduce FinDR, a Chinese dataset of financial dispute records, annotated with official judgments on claim reasonableness. Our proposed method, Multi-Scale Model Interaction (MSMI), leverages a lightweight classifier to evaluate outputs and guide iterative refinement by Large Language Models (LLMs). Experimental results demonstrate that MSMI significantly outperforms single-pass prompting strategies. Additionally, we validate the generalizability of MSMI on several short-text benchmarks, showing improved adversarial robustness. Our findings reveal the potential of multi-model collaboration for enhancing legal document generation and broader text refinement tasks.
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