Judge Anything: MLLM as a Judge Across Any Modality
Evaluating generative foundation models on open-ended multimodal
understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g.,
images, audio, video) poses significant challenges due to the complexity of
cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs
(MLLMs) as automated judges has emerged, with encouraging results in assessing
vision-language understanding tasks. Moving further, this paper extends
MLLM-as-a-Judge across modalities to a unified manner by introducing two
benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the
overall performance and judging capabilities of MLLMs across any-to-any
modality tasks. Specifically, TaskAnything evaluates the MMU and MMG
capabilities across 15 any-to-any modality categories, employing 1,500 queries
curated from well-established benchmarks. Furthermore, JudgeAnything evaluates
the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from
the perspectives of Pair Comparison and Score Evaluation, providing a
standardized testbed that incorporates human judgments and detailed rubrics.
Our extensive experiments reveal that while these MLLMs show promise in
assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting
and 42.79% in Score Evaluation setting), they encounter significant challenges
with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and
30.05% in Score Evaluation setting), exposing cross-modality biases and
hallucination issues. To address this, we present OmniArena, an automated
platform for evaluating omni-models and multimodal reward models. Our work
highlights the need for fairer evaluation protocols and stronger alignment with
human preferences. The source code and dataset are publicly available at:
https://urrealhero.github.io/judgeanythingweb/.
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Shu Pu
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Shu Pu
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pudashi
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Yaochen Wang
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Dongping Chen
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Dongping Chen
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Yuhang Chen
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2025-03-25T09:21:02.911Z
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Yuhang Chen
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Guohao Wang
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Guohao Wang
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NiuniuWang
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Qi Qin
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Zhongyi Zhang
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Zhiyuan Zhang
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Zetong Zhou
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2025-03-25T09:21:29.118Z
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Zetong Zhou
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Frywind
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Shuang Gong
admin_assigned
2025-03-25T09:21:35.465Z
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Shuang Gong
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shuang72
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Yi Gui
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2025-08-06T19:38:11.813Z
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Yi Gui
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starmage520
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Yao Wan
67e21f300e6b6fcc3eb38aed
Philip S. Yu
Judge Anything: MLLM as a Judge Across Any Modality
Evaluating generative foundation models on open-ended multimodal
understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g.,
images, audio, video) poses significant challenges due to the complexity of
cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs
(MLLMs) as automated judges has emerged, with encouraging results in assessing
vision-language understanding tasks. Moving further, this paper extends
MLLM-as-a-Judge across modalities to a unified manner by introducing two
benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the
overall performance and judging capabilities of MLLMs across any-to-any
modality tasks. Specifically, TaskAnything evaluates the MMU and MMG
capabilities across 15 any-to-any modality categories, employing 1,500 queries
curated from well-established benchmarks. Furthermore, JudgeAnything evaluates
the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from
the perspectives of Pair Comparison and Score Evaluation, providing a
standardized testbed that incorporates human judgments and detailed rubrics.
Our extensive experiments reveal that while these MLLMs show promise in
assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting
and 42.79% in Score Evaluation setting), they encounter significant challenges
with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and
30.05% in Score Evaluation setting), exposing cross-modality biases and
hallucination issues. To address this, we present OmniArena, an automated
platform for evaluating omni-models and multimodal reward models. Our work
highlights the need for fairer evaluation protocols and stronger alignment with
human preferences. The source code and dataset are publicly available at:
https://urrealhero.github.io/judgeanythingweb/.
MLLMs evaluate cross-modal understanding and generation tasks across various modalities but face challenges with MMG tasks, leading to the proposal of OmniArena for more comprehensive evaluation.
Multimodal LLMs MLLM-as-a-Judge TaskAnything JudgeAnything MMU MMG Pair Comparison Score Evaluation OmniArena cross-modality biases hallucination issues