What is the robustness of SMoES-based MoE-VLMs to distribution shift and adversarial inputs compared to dense

Abstract

Abstract: Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed b

Research Question

What is the robustness of SMoES-based MoE-VLMs to distribution shift and adversarial inputs compared to dense VLMs of equivalent inference FLOPs on multimodal benchmarks?

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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