Efficiency Improvement of RAG based SLM for Edge Devices

Abstract

The increasing need to deploy language models on constrained devices has given rise to efficiency issues in retrieval-augmented generation (RAG) approaches. Although RAGs boost answers’ quality by retrieving knowledge from external sources, current methods utilize static retrieval mechanisms, resulting in unnecessary computation, higher latencies, and inefficiency in resource usage. In this work, an efficient RAG approach based on small language models (SLMs) is presented, which uses a efficient and adaptive retrieval scheme. This method dynamically changes the retrieval depth and context constrution based on the complexity of the query, using a trained MLP router whose routing decisions are learned from Adaptive-RAG-style oracle labels rather than hand-written rules, leading to a compromise between performance and efficiency. A full pipeline is provided, including dataset preprocessing, corpus generation, embedding construction, vector indexation, retrieval, and answer generation processes. Experiments were performed on HotpotQA bench mark and SQuAD 2.0 datasets, comparing the presented approach with the baseline RAG approach using static retrieval scheme. Experimental results show that the proposed approach lowers the computation cost while providing similar answers’ quality. By adaptively controlling retrieval and context size, the framework provides an effective solution for deploying RAG systems in constrained environments.