Enhancing Large Language Model Reasoning via Retrieval-Augmented Generation and Self-Verification Mechanisms

Abstract

This study proposes a Retrieval Augmented Generation–Self Verification (RAG–SV) framework to enhance the reasoning reliability and factual accuracy of large language models in knowledge intensive tasks. The framework combines external evidence retrieval with a self-verification mechanism that evaluates and refines the model's own responses before final output. Experiments on open domain question answering, fact verification, and multi-step reasoning tasks show that the proposed approach achieves higher Exact Match and F1 scores while significantly reducing hallucination compared with standard LLMs, retrieval augmented baselines, and self-verification only models. Human evaluation further indicates that RAG–SV outputs are more accurate, coherent, and closely aligned with the underlying evidence. The framework is particularly suitable for high stake domains such as education, healthcare, and law, where correctness and explainability are critical. The study concludes that integrating retrieval with reflective self-checking offers a practical path toward more robust, trustworthy, and evidence-based language generation.

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