Contriever and DPR Inference Latency Scaling with Context Windows up to 2048 Tokens

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the inference latency of Contriever and DPR encoders scale with increasing context window sizes up to 2048 tokens on the SQuAD 2.0 benchmark. Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense. 9 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference latency of Contriever and DPR encoders scale with increasing context window sizes up to 2048 tokens on the SQuAD 2.0 benchmark?

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