Concept-Based RAG Models: A High-Accuracy Fact Retrieval Approach (2025)

Abstract: This study introduces a concept-based methodology to optimize Retrieval-Augmented Generation (RAG) tasks by assessing dataset certainty using entropy-based metrics and concept extraction techniques. Unlike traditional methods focused on reducing LLM hallucinations or modifying data structures, this approach evaluates inherent knowledge uncertainty from an LLM perspective. By pre-processing documents with LLMs, the concept-based method significantly enhances precision in tasks demanding high accuracy, such as legal, finance, or formal document responses.

## introduction retrieval-augmented generation (rag) is an advanced framework that combines generative ai models with external retrieval capabilities to provide answers with higher accuracy and contextual relevance. this paper introduces several common types of rag models and analyzes their core features, application scenarios, and technical architecture. these types include standard rag @xcite , @xcite , graph rag @xcite , agentic rag @xcite , and dynamic hierarchical rag @xcite , each showcasing powerful retrieval and generation capabilities across different application domains, catering to diverse information needs. however, these rag models are constrained by the requirement that the dataset must have consistent and meaningful content throughout (low entropy); otherwise, these rag models cannot ensure that the referenced information is derived from the correct documents. ## conclusions and future work the results confirm that the proposed conceptbased bm25 method significantly enhances the precision of term-based models (such as bm25) in semantic matching tasks. this demonstrates the effectiveness of integrating conceptual segmentation as a pre-processing step to address semantic alignment challenges. in future work, we plan to incorporate document entropy as an additional evaluation metric. this will enable more sophisticated selection and utilization of datasets for vector-based or graph-based retrieval methods, further improving the accuracy of selecting relevant documents for generation tasks. notably, the proposed method operates as a preprocessing step and does not occupy inference time, making it highly practical and efficient for integration into existing retrieval-augmented generation workflows.