MMVetGPT4Turbo
MM-Vet evaluation using GPT-4 Turbo for scoring. This variant of MM-Vet examines large multimodal models on complicated multimodal tasks requiring integrated capabilities across six core vision-language abilities: recognition, knowledge, spatial awareness, language generation, OCR, and math.
Qwen2-VL-72B-Instruct from Alibaba Cloud / Qwen Team currently leads the MMVetGPT4Turbo leaderboard with a score of 0.740 across 1 evaluated AI models.
What MMVetGPT4Turbo measures
MMVetGPT4Turbo is a multimodal benchmark that evaluates large language models on math, multimodal, reasoning, spatial reasoning, general, and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.7, with the leader reaching 0.7.
Compare leaders on the best AI for math, best AI for multimodal, best AI for reasoning, best AI for spatial reasoning, best AI for general and best AI for vision leaderboards.
Publication
- Paper
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
- Authors
- Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, and 4 others
- Published
- arXiv
- 2308.02490
Abstract
We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models.
Qwen2-VL-72B-Instruct leads with 74.0%.
Progress Over Time
Interactive timeline showing model performance evolution on MMVetGPT4Turbo
MMVetGPT4Turbo Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 73B | — | — |
FAQ
Common questions about MMVetGPT4Turbo.
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