What is the comparative performance drop in Video-MME accuracy for MoE models (e.g., Mixtral 8x22B) versus den

Abstract

Abstract: We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69\% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up version of the one used for phi-2, composed of heavily filtered publicly available web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide param

Research Question

What is the comparative performance drop in Video-MME accuracy for MoE models (e.g., Mixtral 8x22B) versus dense models (e.g., Llama 3 70B) as video context length increases from 128K to 10M tokens across different temporal granularities?

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