LGM：利用概念元关系与迭代检索增强大型语言模型
LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval
AI总结：
本文提出语言图模型LGM，通过提取继承、别名和组合等概念元关系并结合反思验证与概念迭代检索算法，使LLM无需截断即可处理任意长度文本，在标准基准上持续超越现有RAG方法。
摘要：
大型语言模型（LLMs）表现出强大的语义理解能力，但在用户指令涉及模糊或概念上不一致的术语时仍然面临困难。我们提出语言图模型（Language Graph Model，LGM），通过从自然语言中提取元关系——继承、别名和组合——来增强概念清晰度。该模型进一步采用反思机制来验证这些元关系。借助概念迭代检索算法（Concept Iterative Retrieval Algorithm），这些关系及相关描述被动态提供给LLM，从而提高其解释概念和生成准确响应的能力。与依赖扩展上下文窗口的传统检索增强生成（Retrieval-Augmented Generation，RAG）方法不同，我们的方法使大型语言模型能够处理任意长度的文本，而无需截断。在标准基准上的实验表明，LGM持续优于现有的RAG基线。
英文摘要：
Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language Graph Model (LGM) to enhance conceptual clarity by extracting meta-relations-inheritance, alias, and composition-from natural language. The model further employs a reflection mechanism to validate these meta-relations. Leveraging a Concept Iterative Retrieval Algorithm, these relations and related descriptions are dynamically supplied to the LLM, improving its ability to interpret concepts and generate accurate responses. Unlike conventional Retrieval-Augmented Generation (RAG) approaches that rely on extended context windows, our method enables large language models to process texts of any length without the need for truncation. Experiments on standard benchmarks demonstrate that the LGM consistently outperforms existing RAG baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
