Multi-Objective Evaluation of Multimodal Models on Accuracy-Fairness Trade-offs

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the multi-objective evaluation framework perform when applied to multimodal models (e.g., CLIP or Flamingo) in terms of accuracy and fairness trade-offs on visual-linguistic benchmarks like. 5 claims were extracted from source literature; 4 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the multi-objective evaluation framework perform when applied to multimodal models (e.g., CLIP or Flamingo) in terms of accuracy and fairness trade-offs on visual-linguistic benchmarks like VQAv2 or COCO-QA compared to text-only models?

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