MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
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. Code and data are
available at https://github.com/yuweihao/MM-Vet.
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Weihao Yu
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2023-08-07T12:50:19.998Z
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Weihao Yu
user
whyu
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Zhengyuan Yang
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2023-08-07T12:50:47.078Z
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Zhengyuan Yang
user
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Linjie Li
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2023-08-07T12:51:04.437Z
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Linjie Li
user
linjieli222
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Jianfeng Wang
admin_assigned
2023-08-07T12:51:28.557Z
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Jianfeng Wang
user
amsword8
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Kevin Lin
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2023-08-08T01:19:37.040Z
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Kevin Lin
user
kevinlin311tw
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Zicheng Liu
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Xinchao Wang
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Lijuan Wang
admin_assigned
2023-08-07T12:52:29.085Z
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Lijuan Wang
user
Lijuan
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
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. Code and data are
available at https://github.com/yuweihao/MM-Vet.
MM-Vet evaluates large multimodal models across complicated tasks using defined vision-language capabilities and a unified LLM-based evaluator.
multimodal models VL capabilities LLM-based evaluator vision-language