Parrot: Multilingual Visual Instruction Tuning
The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V
has marked a significant step towards artificial general intelligence. Existing
methods mainly focus on aligning vision encoders with LLMs through supervised
fine-tuning (SFT) to endow LLMs with multimodal abilities, making MLLMs'
inherent ability to react to multiple languages progressively deteriorate as
the training process evolves. We empirically find that the imbalanced SFT
datasets, primarily composed of English-centric image-text pairs, lead to
significantly reduced performance in non-English languages. This is due to the
failure of aligning the vision encoder and LLM with multilingual tokens during
the SFT process. In this paper, we introduce Parrot, a novel method that
utilizes textual guidance to drive visual token alignment at the language
level. Parrot makes the visual tokens condition on diverse language inputs and
uses Mixture-of-Experts (MoE) to promote the alignment of multilingual tokens.
Specifically, to enhance non-English visual tokens alignment, we compute the
cross-attention using the initial visual features and textual embeddings, the
result of which is then fed into the MoE router to select the most relevant
experts. The selected experts subsequently convert the initial visual tokens
into language-specific visual tokens. Moreover, considering the current lack of
benchmarks for evaluating multilingual capabilities within the field, we
collect and make available a Massive Multilingual Multimodal Benchmark which
includes 6 languages, 15 categories, and 12,000 questions, named as MMMB. Our
method not only demonstrates state-of-the-art performance on multilingual
MMBench and MMMB, but also excels across a broad range of multimodal tasks.
Both the source code and the training dataset of Parrot will be made publicly
available.
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Hai-Long Sun
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2024-06-05T07:30:37.957Z
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Allen Sun
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Da-Wei Zhou
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Da-Wei Zhou
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Yang Li
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2024-06-06T09:27:32.961Z
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liyang
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Shiyin Lu
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Shiyin Lu
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Chao Yi
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Qing-Guo Chen
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Qing-Guo Chen
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Zhao Xu
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Weihua Luo
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Kaifu Zhang
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2024-06-06T08:27:52.743Z
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Kaifu Zhang
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De-Chuan Zhan
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Han-Jia Ye
Parrot: Multilingual Visual Instruction Tuning
The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V
has marked a significant step towards artificial general intelligence. Existing
methods mainly focus on aligning vision encoders with LLMs through supervised
fine-tuning (SFT) to endow LLMs with multimodal abilities, making MLLMs'
inherent ability to react to multiple languages progressively deteriorate as
the training process evolves. We empirically find that the imbalanced SFT
datasets, primarily composed of English-centric image-text pairs, lead to
significantly reduced performance in non-English languages. This is due to the
failure of aligning the vision encoder and LLM with multilingual tokens during
the SFT process. In this paper, we introduce Parrot, a novel method that
utilizes textual guidance to drive visual token alignment at the language
level. Parrot makes the visual tokens condition on diverse language inputs and
uses Mixture-of-Experts (MoE) to promote the alignment of multilingual tokens.
Specifically, to enhance non-English visual tokens alignment, we compute the
cross-attention using the initial visual features and textual embeddings, the
result of which is then fed into the MoE router to select the most relevant
experts. The selected experts subsequently convert the initial visual tokens
into language-specific visual tokens. Moreover, considering the current lack of
benchmarks for evaluating multilingual capabilities within the field, we
collect and make available a Massive Multilingual Multimodal Benchmark which
includes 6 languages, 15 categories, and 12,000 questions, named as MMMB. Our
method not only demonstrates state-of-the-art performance on multilingual
MMBench and MMMB, but also excels across a broad range of multimodal tasks.
Both the source code and the training dataset of Parrot will be made publicly
available.
Parrot enhances multimodal language models with multilingual visual token alignment using textual guidance and Mixture-of-Experts, achieving state-of-the-art performance on multilingual benchmarks.
Multimodal Large Language Models MLLMs supervised fine-tuning SFT vision encoders multilingual tokens Parrot textual guidance visual token alignment Mixture-of-Experts MoE cross-attention language-specific visual tokens MMMB