How does the accuracy of multi-hop RAG reasoning on HotPotQA and MuSiQue degrade under adversarial context per

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Abstract: Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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

How does the accuracy of multi-hop RAG reasoning on HotPotQA and MuSiQue degrade under adversarial context perturbations when using dense retrievers (e.g., DPR) versus sparse retrievers (e.g., BM25), measured by F1 and EM scores?

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Correction Record

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Provenance