用户协作编辑模型，让大模型快速适配金融等专业领域。
Collaborative Editable Model
- 用用户贡献的片段构建知识池，通过对话和评分筛选高价值内容。
- 在金融场景中用1.5万条反馈验证，生成质量显著提升。
- 无需传统微调，轻量级适配适合需要快速迭代的专业场景。
垂直领域大型语言模型在金融、医疗、法律等专业场景中至关重要，但其训练常依赖大规模标注数据和大量计算资源，阻碍快速开发与持续迭代。为此，我们提出协作可编辑模型（CoEM），从用户贡献的领域片段中构建候选知识池，结合用户与模型的交互对话、用户评分及归属分析，精准识别高价值知识片段，并通过上下文提示注入实现轻量化领域适配。利用高价值知识，模型可生成更准确、更具领域特性的内容。在金融信息场景中，我们收集约120名用户的1.5万条反馈，通过用户评分验证CoEM效果，证明其在领域生成质量上显著优于传统方法，且避免了传统微调流程的时间与算力开销。
原文摘要 · Abstract (English)
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues combined with user ratings and attribution analysis to pinpoint high-value knowledge fragments, and injects these fragments via in-context prompts for lightweight domain adaptation. With high-value knowledge, the LLM can generate more accurate and domain-specific content. In a financial information scenario, we collect 15k feedback from about 120 users and validate CoEM with user ratings to assess the quality of generated insights, demonstrating significant improvements in domain-specific generation while avoiding the time and compute overhead of traditional fine-tuning workflows.
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