How do different retrieval evaluation strategies (e.g., recall-based vs relevance-based) affect the downstream
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Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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 goal: How do different retrieval evaluation strategies (e.g., recall-based vs relevance-based) affect the downstream QA accuracy and robustness of LLM-based RAG systems on multi-hop queries from MuSiQue and HotPotQA? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.
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