How does the inference efficiency (tokens/sec and memory usage) of a 70B-parameter LLM agent compare when usin

Abstract

Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval. This type of fine-tuning is highly resource-intensive and does not enable the use of larger LLMs. In this work, we propose Q-RAG, a novel

Research Question

How does the inference efficiency (tokens/sec and memory usage) of a 70B-parameter LLM agent compare when using a 2-step retrieval-augmented generation pipeline versus a long-context-only approach on the MuSiQue multi-hop QA dataset?

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