Scaling AdaptToken from 3B to 7B Parameters: Accuracy-Latency Trade-offs on AdvGLUE with Adversarial Training

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the impact of scaling AdaptToken-3B's model size (e.g., 3B to 7B parameters) on AdvGLUE benchmark performance when combining mean shift-based feature space analysis with adversarial training,. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of scaling AdaptToken-3B's model size (e.g., 3B to 7B parameters) on AdvGLUE benchmark performance when combining mean shift-based feature space analysis with adversarial training, measured by accuracy and inference latency?

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