Multimodal Retriever Portfolios Enhance RAG Performance on AmbiEval

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: To what extent do retriever portfolios improve performance on the AmbiEval benchmark when applied to multimodal RAG systems (e.g., combining text and image retrieval), and how does this compare to. 13 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent do retriever portfolios improve performance on the AmbiEval benchmark when applied to multimodal RAG systems (e.g., combining text and image retrieval), and how does this compare to single-retriever multimodal systems in terms of accuracy and latency trade-offs?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

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