Curriculum Engineering: Structured Learning for Large Language Models (LLMs) Through Curriculum Based Retrieval

Social manufacturing integrates social resources with manufacturing, demanding rapid decision-making by processing specialized and complex data to adapt market changes. Leveraging multisource information as external knowledge bases, implementing Retrieval-Augmented Generation (RAG) to Large Language Models (LLMs) holds great potential to enhance the manufacturing efficiency. However, existing RAG methods often return excessive unstructured information, limiting LLMs’ ability to reason and solve complex question-and-answer (QA) tasks. Drawing inspiration from structured human learning, we propose a novel Curriculum Enhanced Retrieval-Augmented Generation (CE-RAG) framework. CE-RAG encompasses three stages: The syllabus setting phase for organizing subjects and study sequences for the knowledge base, the knowledge infilling phase for generating curriculum chain prompts for LLMs, and the curriculum review phase for memorizing the curriculum chains learned by LLMs to improve iterative retrieval. Experiments on public QA reasoning datasets and social manufacturing cases show CE-RAG can effectively improve the reasoning performance of LLMs in complex social manufacturing QA tasks.