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.
Figure 1: The overall architecture of Parrot. It converts English-biased features to language-specific features based on the multilingual MoE module, aiming to improve the multilingual capabilities. The training details within each stage are presented on the right.
There are several existing multilingual benchmarks (i.e., Multi30K, M3Exam, MMBench, and LLaVA-Bench) for MLLMs, but they have some limitations:
Figure 2: Some bad cases for multilingual benchmarkperceive. Left: code reasoning is strongly related to English. Middle: logical reasoning is too challenging. Right: lack relevance between image and text.
We selected six languages for inclusion: English (en), Chinese (zh), Portuguese (pt), Arabic (ar), Turkish (tr), and Russian (ru). These languages represent a diverse range of linguistic families, and we list the detailed information and some multilingual cases in Figure 3. In terms of dataset requirements and consistency, our benchmark incorporates datasets in two main respects:
Figure 3: Overview of MMMB. It incorporates 6 languages, 15 categories, and 12,000 questions.
To enhance the intuitive understanding of the Parrot's multilingual capability, we prepare a comprehensive case study accompanied by illustrative visuals.
@article{sun2024parrot,
title={Parrot: Multilingual Visual Instruction Tuning},
author={Sun, Hai-Long and Zhou, Da-Wei and Li, Yang and Lu, Shiyin and Yi, Chao and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu and Zhan, De-Chuan and others},
journal={arXiv preprint arXiv:2406.02539},
year={2024}
}