Sparse Multimodal Model Efficiency and Alignment Trade-offs on VQAv2 and OK-VQA

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Does the inference efficiency of sparse multimodal models with varying numbers of experts improve with higher alignment scores on VQAv2 and OK-VQA, and how does this trade-off compare to dense models. Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Does the inference efficiency of sparse multimodal models with varying numbers of experts improve with higher alignment scores on VQAv2 and OK-VQA, and how does this trade-off compare to dense 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