Contriever and DPR Retrieval Accuracy on Natural Questions at 2048-Token Context

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the retrieval accuracy of Contriever and DPR encoders compare on the Natural Questions benchmark when the context window size is increased to 2048 tokens. Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR) system. RAG has become. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the retrieval accuracy of Contriever and DPR encoders compare on the Natural Questions benchmark when the context window size is increased to 2048 tokens?

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