How does the end-to-end latency of RAG systems with 128K context windows compare to iterative retrieval with B

Abstract

Abstract: Retrieval augmented generation (RAG) with large language models (LLMs) for Question Answering (QA) entails furnishing relevant context within the prompt to facilitate the LLM in answer generation. During the generation, inaccuracies or hallucinations frequently occur due to two primary factors: inadequate or distracting context in the prompts, and the inability of LLMs to effectively reason through the facts. In this paper, we investigate whether providing aligned context via a carefully selected passage sequence leads to better answer generation by the LLM for multi-hop QA. We introduce, "Gen

Research Question

How does the end-to-end latency of RAG systems with 128K context windows compare to iterative retrieval with BM25 re-ranking in terms of exact match scores on HotpotQA when evaluated with adversarial distractors?

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