Manifold-Aware Embeddings for Cross-Lingual Dense Retrieval on XQuAD

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Do manifold-aware embeddings derived from Wikipedia-based semantic relatedness metrics improve cross-lingual dense retrieval performance on XQuAD compared to standard cosine similarity, as measured. Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query-passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on. 18 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Do manifold-aware embeddings derived from Wikipedia-based semantic relatedness metrics improve cross-lingual dense retrieval performance on XQuAD compared to standard cosine similarity, as measured by MRR@10 and exact match accuracy?

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