The Mitigation of Excessive Retrieval Augmentation and Knowledge Conflicts in Large Language Models

There have been significant advances with large language models in generating coherent and contextually relevant responses, but their limitations in accessing and integrating real-time or specialized information have driven the development of retrieval augmentation techniques. Retrieval augmentation offers the ability to enhance a model’s responses through external knowledge, yet the challenge of managing knowledge conflicts between retrieved data and internal model predictions remains unresolved. A systematic examination of varying levels of retrieval augmentation has revealed that excessive reliance on external information not only introduces factual inconsistencies but also degrades the coherence of model outputs. The experiments conducted on the Llama model demonstrate that while moderate augmentation improves accuracy and relevance, high retrieval augmentation significantly increases the risk of knowledge conflicts, complicating the response generation process. The conflict detection and resolution mechanisms employed showed promise in mitigating some of these inconsistencies, although their effectiveness diminished as the volume of retrieval data increased. These findings highlight the delicate balance required between external knowledge integration and internal model coherence, emphasizing the need for more sophisticated conflict management strategies to optimize the potential of retrieval-augmented models.