Lexical Injection Impact on Cross-Lingual Retrieval Efficiency in Edge Deployment

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the lexical injection approach proposed in this paper affect the inference efficiency of cross-lingual retrieval models when deployed on edge devices, as measured by latency and throughput. Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely. 8 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the lexical injection approach proposed in this paper affect the inference efficiency of cross-lingual retrieval models when deployed on edge devices, as measured by latency and throughput on the FLORES-200 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