How do evidence gap identification mechanisms in FAIR-RAG systems affect F1 score performance on HotpotQA when

Abstract

Abstract: While Retrieval-Augmented Generation (RAG) mitigates hallucination and knowledge staleness in Large Language Models (LLMs), existing frameworks often falter on complex, multi-hop queries that require synthesizing information from disparate sources. Current advanced RAG methods, employing iterative or adaptive strategies, lack a robust mechanism to systematically identify and fill evidence gaps, often propagating noise or failing to gather a comprehensive context. We introduce FAIR-RAG, a novel agentic framework that transforms the standard RAG pipeline into a dynamic, evidence-driven reasoning

Research Question

How do evidence gap identification mechanisms in FAIR-RAG systems affect F1 score performance on HotpotQA when comparing single-pass versus iterative retrieval strategies?

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Provenance