Self-Knowledge Guided Retrieval Augmentation for Large Language Models

Understand

Large language models (LLMs) have shown superior performance without task-specific fine-tuning.

• Despite the success, the knowledge stored in the parameters of LLMs could still be incomplete and difficult to update due to the computational costs.
• As complementary, retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering.
• However, we find that the retrieved knowledge does not always help and even has a negative impact on original responses occasionally.