Scaling Document Retrieval in Hybrid Models: Accuracy-Efficiency Trade-offs in QMSum

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the trade-off between retrieval accuracy and inference efficiency compare in hybrid retrieval-augmented models when scaling the number of documents in QMSum, and what metrics best capture. 10 claims were extracted from source literature; 9 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 the trade-off between retrieval accuracy and inference efficiency compare in hybrid retrieval-augmented models when scaling the number of documents in QMSum, and what metrics best capture this trade-off?

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