Does the Tree of Reviews iterative retrieval method improve robustness to irrelevant context in multi-hop QA c

Abstract

Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) for domain-specific question-answering (QA) tasks by leveraging external knowledge sources. However, traditional RAG systems primarily focus on relevance-based retrieval and often struggle with redundancy, especially when reasoning requires connecting information from multiple sources. This paper introduces Vendi-RAG, a framework based on an iterative process that jointly optimizes retrieval diversity and answer quality. This joint optimization leads to significantly higher accuracy for multi-hop QA tasks. Vendi-RAG lev

Research Question

Does the Tree of Reviews iterative retrieval method improve robustness to irrelevant context in multi-hop QA compared to single-step retrieval on the 2WikiMultihop dataset, measured by F1 score and precision?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance