From External Similarity to Internal Consistency: An Enhanced Retrieval-Based Method for LLMs' Reliable Content Generation

Artificial Intelligence Generated Content (AIGC) has emerged as a mainstream research direction with the development of Large Language Models (LLMs). The hallucination of LLMs, however, always interweaves the generated content with outdated or fabricated information, making it hard to be fully trusted and severely hindering LLMs form being widely applied in real-life scenarios. To address this problem, Retrieval Augmented Generation (RAG) has been proposed, which incorporates external knowledge to assist LLMs with content generation and significantly alleviates the hallucination problem. Nonetheless, the vanilla RAG uses similarity as the sole criterion for selecting external knowledge, neglecting the problem of internal inconsistency within the knowledge itself, which may distract LLMs from focusing on the most important information during the content generation process and, therefore, has a negative impact on the generated content's reliability. In this paper, we propose a novel metric, Entropy-based Internal Consistency (EIC), to measure the internal consistency of the external knowledge which is then integrated with similarity to mutually determine the knowledge's importance. Experimental results demonstrate that the proposed metric can provide a more fine-grained signal for external knowledge selection, thereby enhancing the reliability of generated content.