Dense vs. Sparse Retrievers in RAG Systems: Factual Consistency on MS MARCO

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the integration of dense retrievers like MA-DPR versus sparse lexical methods impact the factual consistency scores of RAG systems on the MS MARCO dataset. This paper proposes a Question-Answering (QA) system for the telecom domain using 3rd Generation Partnership Project (3GPP) technical documents. Alongside, a hybrid dataset, Telco-DPR, which consists of a curated 3GPP corpus in a hybrid format, combining text and tables, is. 11 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the integration of dense retrievers like MA-DPR versus sparse lexical methods impact the factual consistency scores of RAG systems on the MS MARCO dataset?

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