Intriguing Properties of Large Language and Vision Models
Recently, large language and vision models (LLVMs) have received significant
attention and development efforts due to their remarkable generalization
performance across a wide range of tasks requiring perception and cognitive
abilities. A key factor behind their success is their simple architecture,
which consists of a vision encoder, a projector, and a large language model
(LLM). Despite their achievements in advanced reasoning tasks, their
performance on fundamental perception-related tasks (e.g., MMVP) remains
surprisingly low. This discrepancy raises the question of how LLVMs truly
perceive images and exploit the advantages of the vision encoder. To address
this, we systematically investigate this question regarding several aspects:
permutation invariance, robustness, math reasoning, alignment preserving and
importance, by evaluating the most common LLVM's families (i.e., LLaVA) across
10 evaluation benchmarks. Our extensive experiments reveal several intriguing
properties of current LLVMs: (1) they internally process the image in a global
manner, even when the order of visual patch sequences is randomly permuted; (2)
they are sometimes able to solve math problems without fully perceiving
detailed numerical information; (3) the cross-modal alignment is overfitted to
complex reasoning tasks, thereby, causing them to lose some of the original
perceptual capabilities of their vision encoder; (4) the representation space
in the lower layers (<25%) plays a crucial role in determining performance and
enhancing visual understanding. Lastly, based on the above observations, we
suggest potential future directions for building better LLVMs and constructing
more challenging evaluation benchmarks.
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Young-Jun Lee
claimed_verified
2024-10-09T07:37:29.247Z
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Young-Jun Lee
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passing2961
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Byungsoo Ko
admin_assigned
2024-10-11T10:34:44.659Z
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Byungsoo Ko
user
kobiso
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Han-Gyu Kim
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2024-10-09T01:41:28.430Z
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Han-Gyu Kim
user
mkmiracle
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Yechan Hwang
admin_assigned
2024-10-14T07:54:02.769Z
653032b83ecbe51d6a6f0498
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Yechan Hwang
user
YYXYmint
6705df47f6f1dfb1734d6ee8
Ho-Jin Choi
Intriguing Properties of Large Language and Vision Models
Recently, large language and vision models (LLVMs) have received significant
attention and development efforts due to their remarkable generalization
performance across a wide range of tasks requiring perception and cognitive
abilities. A key factor behind their success is their simple architecture,
which consists of a vision encoder, a projector, and a large language model
(LLM). Despite their achievements in advanced reasoning tasks, their
performance on fundamental perception-related tasks (e.g., MMVP) remains
surprisingly low. This discrepancy raises the question of how LLVMs truly
perceive images and exploit the advantages of the vision encoder. To address
this, we systematically investigate this question regarding several aspects:
permutation invariance, robustness, math reasoning, alignment preserving and
importance, by evaluating the most common LLVM's families (i.e., LLaVA) across
10 evaluation benchmarks. Our extensive experiments reveal several intriguing
properties of current LLVMs: (1) they internally process the image in a global
manner, even when the order of visual patch sequences is randomly permuted; (2)
they are sometimes able to solve math problems without fully perceiving
detailed numerical information; (3) the cross-modal alignment is overfitted to
complex reasoning tasks, thereby, causing them to lose some of the original
perceptual capabilities of their vision encoder; (4) the representation space
in the lower layers (<25%) plays a crucial role in determining performance and
enhancing visual understanding. Lastly, based on the above observations, we
suggest potential future directions for building better LLVMs and constructing
more challenging evaluation benchmarks.
The investigation into large language and vision models reveals that while they excel in advanced reasoning, they may lack robust perception, as seen through various benchmarks evaluating different aspects like permutation invariance and cross-modal alignment.
large language and vision models LLVMs vision encoder projector large language model MMVP permutation invariance math reasoning cross-modal alignment visual patch sequences visual understanding