Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs

Understand

Hallucinations present a significant challenge for large language models (LLMs).

• The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations.
• While incorporating external information can help fill knowledge gaps, it also introduces the risk of irrelevant information, thereby increasing the likelihood of external hallucinations.
• To balance the use of parametric knowledge within LLMs and external information, in this study, we present Rowen, a novel framework that enhances LLMs with an adaptive retrieval augmentation process tailored to address hallucinated outputs.