Cross-Lingual F1-Score Gaps in Gemma2 Models on Adversarial QA Datasets

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the F1-score gap between English and Italian QA tasks scale when comparing Gemma2-2B and Gemma2-7B on adversarial cross-lingual datasets generated via beam search. 13 claims were extracted from source literature; 4 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the F1-score gap between English and Italian QA tasks scale when comparing Gemma2-2B and Gemma2-7B on adversarial cross-lingual datasets generated via beam search?

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