Few-Shot Prompting in Masked vs. Autoregressive Models for Cross-Lingual Named Entity Recognition

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does few-shot prompting performance of masked language models compare to autoregressive models on cross-lingual named entity recognition benchmarks for low-resource languages. 6 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does few-shot prompting performance of masked language models compare to autoregressive models on cross-lingual named entity recognition benchmarks for low-resource languages?

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