What is the relative accuracy drop of decomposed vs. non-decomposed multi-hop RAG systems under adversarial qu

Abstract

Abstract: Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of exte

Research Question

What is the relative accuracy drop of decomposed vs. non-decomposed multi-hop RAG systems under adversarial query perturbations, measured on the HotpotQA dataset with BM25 and DPR retrievers?

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