用大模型和检索增强生成技术，自动解析金融衍生品合同并转为标准结构数据。
AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts
- 基于模板与分层检索生成，确保输出符合预设结构规范。
- 在场外金融衍生品合同上验证，实现高效结构化转换与准确性评估。
- 适合金融、法律AI领域，提升合同自动化处理能力。
大型语言模型（LLMs）和检索增强生成（RAG）正在重塑AI从非结构化文本中提取与组织信息的方式。核心挑战在于设计可增量提取、结构化并验证信息的AI方法，同时保留层级与上下文关系。我们提出CDMizer，一种基于模板、结合LLM与RAG的结构化文本转换框架。通过深度检索与分层生成机制，CDMizer实现受控、模块化的处理流程，使生成内容严格匹配预定义模式。其模板驱动设计保障语法正确性、模式一致性与可扩展性，克服了直接生成方法的关键局限。此外，我们构建了一个基于LLM的评估框架，用于衡量结构化表示的完整性和准确性。在将场外（OTC）金融衍生品合同转换为通用域模型（CDM）的实验中，CDMizer建立了可扩展的AI驱动文档理解与自动化验证基础，适用于更广泛的应用场景。
原文摘要 · Abstract (English)
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are reshaping how AI systems extract and organize information from unstructured text. A key challenge is designing AI methods that can incrementally extract, structure, and validate information while preserving hierarchical and contextual relationships. We introduce CDMizer, a template-driven, LLM, and RAG-based framework for structured text transformation. By leveraging depth-based retrieval and hierarchical generation, CDMizer ensures a controlled, modular process that aligns generated outputs with predefined schema. Its template-driven approach guarantees syntactic correctness, schema adherence, and improved scalability, addressing key limitations of direct generation methods. Additionally, we propose an LLM-powered evaluation framework to assess the completeness and accuracy of structured representations. Demonstrated in the transformation of Over-the-Counter (OTC) financial derivative contracts into the Common Domain Model (CDM), CDMizer establishes a scalable foundation for AI-driven document understanding, structured synthesis, and automated validation in broader contexts.
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。