Riemannian Manifold Metrics Improve mDPR Retrieval Accuracy in Low-Resource Languages

Abstract

Abstract: This report synthesises findings from 5 peer-reviewed papers addressing the following research question: How does applying Riemannian manifold metrics to mDPR embeddings affect retrieval accuracy on the XOR-TyDi QA benchmark for Amharic and Kannada compared to standard Euclidean distance. Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does applying Riemannian manifold metrics to mDPR embeddings affect retrieval accuracy on the XOR-TyDi QA benchmark for Amharic and Kannada compared to standard Euclidean distance?

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