LogicTree-RAG：面向长篇专利撰写的逻辑树引导检索增强生成
LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting
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中文总结 AI 辅助
LogicTree-RAG通过诱导层次化逻辑树作为全局骨干，以证据引导递归生成节点并混合遍历映射到专利章节，实现无需专家先验的可控均衡长篇专利撰写，显著提升内容质量与语言规范性。
中文摘要 AI 辅助
长篇技术文本生成支撑着知识密集型工作流程，然而由于需要在局部连贯性之外实现全局一致的逻辑结构化和忠实的技术推理，这对大型语言模型（LLMs）而言仍具挑战性。专利撰写是这一挑战的典型实例，要求通过持续的多专家协作，整体生成一份合法合规且技术详尽完备的文档。现有方法往往侧重于部分章节的生成或依赖人工制定的提纲，限制了在现实场景中的可扩展自动化。在本工作中，我们提出了LogicTree-RAG，一种逻辑树引导的检索增强生成框架，该框架诱导生成一棵层次化逻辑树作为全局组织骨干，以组织和锚定技术披露内容，无需依赖专家定义的撰写先验。逻辑树中的每个节点代表一个技术要素，并通过证据引导的递归生成来构建。随后，一种混合遍历机制将逻辑树映射到专利章节，实现可控且章节均衡的生成。大量实验表明，LogicTree-RAG在内容质量和语言规范性上持续优于强基线的基于LLM的方法，并以高令牌效率实现更长的结构化生成，证明了以逻辑为中心的生成方法在复杂技术文档撰写中的有效性。
英文摘要
Long-form technical text generation underpins knowledge-intensive workflows, yet remains challenging for large language models (LLMs) due to the need for globally consistent logical structuring and faithful technical reasoning beyond local coherence. Patent drafting is a canonical instance of this challenge, demanding holistic generation of a legally compliant and technically exhaustive document through sustained multi-expert collaboration. Existing approaches often focus on partial section generation or rely on manually crafted outlines, limiting scalable automation in realistic settings. In this work, we propose LogicTree-RAG, a logic tree-guided retrieval-augmented generation framework that induces a hierarchical logic tree as a global organizational backbone to organize and ground technical disclosures, without relying on expert-defined drafting priors. Each node in the logic tree represents a technical element and is constructed through evidence-guided recursive generation. A hybrid traversal mechanism then maps the logic tree into patent sections, enabling controllable and section-balanced generation. Extensive experiments show that LogicTree-RAG consistently improves content quality and language conformity over strong LLM-based baselines and achieves longer structured generation with high token efficiency, demonstrating the effectiveness of logic-centric generation for complex technical document drafting.
发表机构
- National University of Singapore（新加坡国立大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
