Dimensionality Reduction Effects on Multi-Hop RAG Performance in NaturalQuestions

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does reducing vector embedding dimensionality from 1024 to 256 impact Exact Match and F1 scores on the NaturalQuestions benchmark for multi-hop RAG systems compared to single-hop queries. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does reducing vector embedding dimensionality from 1024 to 256 impact Exact Match and F1 scores on the NaturalQuestions benchmark for multi-hop RAG systems compared to single-hop queries?

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