LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval

2025-11-05

AI Summary

To address the limited semantic understanding of large language models (LLMs) when processing ambiguous or conceptually inconsistent instructions, this paper proposes a language-graph-based conceptual enhancement method. The approach introduces three key contributions: (1) a language graph model that explicitly extracts conceptual meta-relations—including inheritance, aliasing, and composition; (2) a reflective verification mechanism coupled with a conceptual iterative retrieval algorithm, enabling dynamic semantic enhancement of inputs without reliance on extended context windows or external knowledge bases; and (3) end-to-end support for arbitrarily long text inputs. Experimental evaluation across multiple standardized benchmarks demonstrates substantial improvements over state-of-the-art RAG methods, particularly in complex conceptual parsing and response accuracy. The proposed framework establishes a novel paradigm for controllable, concept-aware semantic interpretation in LLMs.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Enhance LLMs' conceptual clarity for ambiguous terms
• Extract meta-relations like inheritance and alias from language
• Enable processing of unlimited text length without truncation

Innovation

• Extracts meta-relations from natural language
• Employs reflection mechanism to validate relations
• Uses iterative retrieval algorithm for dynamic input