Geodesic vs. Euclidean Distance in Dense Retrievers for Large-Scale Language Benchmarks

This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the computational efficiency of dense retrievers using geodesic distance compare to Euclidean distance when scaling to large-scale datasets in language model benchmarks. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the computational efficiency of dense retrievers using geodesic distance compare to Euclidean distance when scaling to large-scale datasets in language model benchmarks?

Autonomous literature synthesis. Automated review score: 9.3/10. Full text and citation available at Assignee Research.