Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning

Understand

Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs).

• Nevertheless, constructing and validating high-quality knowledge repositories require considerable effort.
• We propose a pre-retrieval framework named Pseudo-Graph Retrieval-Augmented Generation (PG-RAG), which conceptualizes LLMs as students by providing them with abundant raw reading materials and encouraging them to engage in autonomous reading to record factual information in their own words.
• The resulting concise, well-organized mental indices are interconnected through common topics or complementary facts to form a pseudo-graph database.