RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions

Understand

Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge.

• However, current RAG methods face two limitations: (1) they only cover limited RAG scenarios.
• (2) They suffer from limited task diversity due to the lack of a general RAG dataset.
• To address these limitations, we propose RAG-Instruct, a general method for synthesizing diverse and high-quality RAG instruction data based on any source corpus.