A Scalable Multi-Agent Framework for Low-Resource E-Commerce Concept Extraction and Standardization

Understanding shopping concepts in e-commerce is hard because consumer behavior, product terms, and technical details vary a lot. This paper presents MALLM, a new multi-agent framework based on LLaMA-2 70B. It improves concept understanding by letting specialized agents work together. The model uses a layered agent system, domain-adaptive pretraining, retrieval-augmented generation (RAG), and cross-modal feature fusion to handle tasks clearly and well. It also applies knowledge distillation and multi-step training to adapt to small data. MALLM balances good results with practical deployment. It works well in real e-commerce situations.