Dynamic RAG Knowledge Base Evolution Latency and Inference Throughput in Code Generation

Abstract

Abstract: Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding LLM responses in structured external information from a knowledge base. However, many KG-based RAG approaches struggle with (i) aligning KG and textual representations, (ii) balancing retrieval accuracy and efficiency, and (iii) adapting to dynamically updated KGs. In this work, we introduce Walk\&Retrieve, a simple yet effective KG-based framework that

Research Question

How does the retrieval latency overhead of dynamic RAG knowledge base evolution affect inference throughput (tokens per second) for code generation tasks on the CodeXGLUE benchmark, relative to static retrieval baselines under controlled GPU memory constraints?

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