Contriever and DPR Retrieval Accuracy on TriviaQA with Extended Context Windows

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 TriviaQA benchmark when the context window size is increased to 4096 tokens. The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense. 17 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.2/10. 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 TriviaQA benchmark when the context window size is increased to 4096 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