Title: Balancing Speed and Precision: Enhancing RAG-Based LLM RecSys with Multi-Head Early Exit Mechanisms

Abstract: Integrating Large Language Models (LLMs) into recommender systems for Click-Through Rate (CTR) prediction requires striking a precise equilibrium between computational efficiency and predictive accuracy. This study introduces an optimization framework that synergizes Retrieval-Augmented Generation (RAG) with a novel multi-head early exit architecture to simultaneously boost performance in both domains. By leveraging Graph Convolutional Networks (GCNs) as high-efficiency retrieval tools, the proposed approach substantially cuts data retrieval latency while preserving strong model performance. The adopted early exit mechanism enables the dynamic conclusion of inference processes by evaluating real-time predictive confidence across various heads. This strategy accelerates LLM responsiveness while maintaining or even elevating accuracy, rendering it particularly suitable for real-time use cases. Experimental results confirm that this architecture effectively minimizes computation time without compromising the accuracy essential for trustworthy recommendations, thereby setting a new benchmark for the efficient, real-time deployment of LLMs in commercial environments.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC