What is the trade-off between inference throughput and multi-hop reasoning accuracy in LLM-based RAG systems w

Abstract

Abstract: Retrieval-augmented generation has raise extensive attention as it is promising to address the limitations of large language models including outdated knowledge and hallucinations. However, retrievers struggle to capture relevance, especially for queries with complex information needs. Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. In this paper, we show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterativ

Research Question

What is the trade-off between inference throughput and multi-hop reasoning accuracy in LLM-based RAG systems when using compressed or distilled retriever models on HotpotQA and MuSiQue?

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