Geodesic Distance vs. Cosine Similarity for Robust Dense Retrieval in BEIR Benchmark

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: Does the adoption of geodesic distance over cosine similarity improve the robustness of dense retrievers against adversarial query perturbations in out-of-distribution settings on the BEIR benchmark. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Does the adoption of geodesic distance over cosine similarity improve the robustness of dense retrievers against adversarial query perturbations in out-of-distribution settings on the BEIR benchmark?

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