Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs

Understand

Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point.

• Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external retrieval.
• While effective for advanced models like GPT-3.5, smaller LLMs face challenges in decomposing complex questions, necessitating supervised fine-tuning.
• Previous work has relied on manual annotation and knowledge distillation from teacher LLMs, which are time-consuming and not accurate enough.