A Study on Improving the Generation Quality of Large Language Model in Question-Answering Systems Using Multi-Population Genetic Algorithm

Existing Retrieval-Augmented Generation (RAG) methods remain insufficient in handling hallucination due to several underlying limitations. First, semantic vector-based retrieval may lead to the loss of critical information from the original text. Second, ambiguous queries can result in the retrieval of unhelpful content. Third, the single-round retrieval strategy cannot simulate the human reasoning process. Therefore, we propose a novel answer reasoning framework that integrates Multi-Population Genetic Algorithms (MPGA) to enhance RAG-based QA systems. The framework first leverages a multi-head self-attention mechanism to capture diverse semantic aspects of the input text. Subsequently, relevant passages retrieved from multiple semantic perspectives are iteratively refined through the optimization process, making them more suitable as contexts for the model. Experimental results demonstrate that our approach effectively exploits semantic diversity across attention heads, leading to responses with improved diversity, completeness, and factual accuracy.