Membership Inference Attack Success in RAG Models with Sparse and Dense Retrieval on HANS

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the membership inference attack success rate compare between RAG models using sparse vs. dense retrieval methods on the HANS dataset. Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. 13 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the membership inference attack success rate compare between RAG models using sparse vs. dense retrieval methods on the HANS dataset?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.