Manifold-Aware Distance Metrics Improve Zero-Shot Cross-Domain Retrieval on BEIR

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does replacing cosine similarity with manifold-aware distance metrics impact the zero-shot cross-domain retrieval accuracy of dense retrievers on the BEIR benchmark compared to standard DPR. Entity retrieval-retrieving information about entity mentions in a query-is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions due to biases towards. 11 claims were extracted from source literature; 10 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 goal: How does replacing cosine similarity with manifold-aware distance metrics impact the zero-shot cross-domain retrieval accuracy of dense retrievers on the BEIR benchmark compared to standard DPR baselines?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.