To what extent does the choice of retriever (BM25 vs. dense passage retriever vs. LLM-based re-ranker) impact

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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 goal: To what extent does the choice of retriever (BM25 vs. dense passage retriever vs. LLM-based re-ranker) impact multi-hop reasoning accuracy in RAG systems on HotpotQA and MuSiQue under varying passage count constraints?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.