Parrot: Multilingual Visual Instruction Tuning

## Abstract

Abstract 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.
Code is available at: https://github.com/AIDC-AI/Parrot .

## 1 Introduction

The rapid development of Large Language Models (LLMs), such as GPT-4 [; ; ; ; ; ; ], has gained significant attention. However, LLMs are limited to processing a single textual modality. The expansion into visual modalities has endowed LLMs with multimodal capabilities [; ; ; ; ; ; ; ; ; ], thereby accelerating the development of Multimodal Large Language Models (MLLMs) and further bringing us closer to the realization of Artificial General Intelligence (AGI).

Current MLLMs mainly rely on pre-trained LLMs and vision encoders, focusing on bridging the modality gap by aligning visual features with language embedding tokens. Existing research predominantly employs either a Q-Former [; ; ] or an MLP projector [; ; ; ; ; ] to align vision encoders with LLMs.
The training data [] mainly consists of English-centric data from image captions and multimodal conversations.
During training, the alignment component converts the visual features into language embedding tokens. With encoded visual features, the LLM can integrate visual information to respond to multimodal inputs.

Multilingual capability in MLLMs entails the ability to generate responses in the same language as the input, catering to the linguistic diversity inherent in conversation. Therefore, it is critically important for processing language-specific content and cultural differences, ensuring equitable access to technological benefits for individuals across diverse regions and nations [; ]. Many LLMs possess multilingual capabilities [; ; ], enabling diverse language responses according to user input. However, after the alignment training of MLLMs, the model may lose its ability to understand, process, or generate in non-English languages, and we call this phenomenon multilingual erosion.
For example, LLaVA [] usually responds in English, regardless of the input language. Therefore, it is essential to enhance MLLM’s multilingual capabilities during multimodal alignment.

Figure: Figure 1: The output of OpenAI-CLIP-based and Chinese-CLIP-based models using the same Chinese prompts. We can observe that the OpenAI-CLIP-based model exhibits confusion between Chinese and English responses.
Refer to caption: x2.png

The main reason for multilingual erosion is that the data used for multimodal alignment is highly imbalanced at the language level.
Due to the dominance of English-centric data, while the model aligns visual and textual tokens well in English, it performs poorly in other languages.
Hence, it is crucial to align visual and textual tokens compatibly at the language level.
We hypothesize that multilingual erosion may arise from the lack of alignment between visual tokens and textual tokens in other languages.
From the perspective of pre-trained datasets, OpenAI-CLIP [] is trained on the large-scale image-text pairs through contrastive learning, with the text corpus being mostly in English, potentially biasing image encoding towards an English semantic space. As shown in Figure 1, we train two separate models using the same data: one with OpenAI-CLIP vision encoder and the other with Chinese-CLIP [] vision encoder. Interestingly, the model equipped with OpenAI-CLIP struggles to generate suitable outputs according to Chinese inputs while the other model with Chinese-CLIP can not only understand the queries but also generate appropriate outputs in Chinese.
Furthermore, we observed an improvement in performance, from 66.4 to 68.3, on the MMBench-CN [] dataset when using Chinese-CLIP.
Therefore, the challenge arises: how to use English-centric multilingual image-text data to bridge the modality gap while enhancing the MLLM’s multilingual capabilities.

Due to the scarcity of non-English multimodal data (*e.g.*, lack of large-scale, high-quality image-text data), we require almost the same amount of image-text data as LLaVA to enhance the model’s multilingual capabilities. Moreover, motivated by preliminary experiments, it is necessary to condition the visual tokens on diverse language inputs. In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level and converts visual tokens into language-specific embeddings using a Mixture-of-Experts (MoE) module [; ]. Specifically, we first calculate the cross-attention between the class token of visual features extracted by the vision encoder and the text embeddings derived from word token embeddings. The result is then passed through the router of MoE to obtain the activated probability distribution of each language expert. Subsequently, demanding the input language, the English-biased visual tokens are converted into language-specific embeddings using the selected experts. This enables Parrot not only to enhance its multilingual capabilities but also to bridge the multimodal gap effectively.

To address the scarcity of current multilingual benchmarks, we introduce a new benchmark encompassing six languages: English, Chinese, Portuguese, Arabic, Turkish, and Russian. This includes an extension of the MMBench-DEV dataset to these six languages and a Massive Multilingual Multimodal Benchmark (MMMB) featuring 2,000 evaluation questions per language, totaling 12,000 questions. We compare several open-source multimodal methods and evaluate some private models. Extensive experiments validate the Parrot’s state-of-the-art performance across two multilingual benchmarks. Specifically in Turkish and Arabic, our method even outperforms LLaVA-NeXT [] by more than 10 percentage points in both benchmarks. Additionally, we evaluate our model across a broad range of multimodal benchmarks (*e.g.*, MME [], ScienceQA-IMG [], and SEED-Bench-IMG []), demonstrating its competitive performance in diversity tasks.

## 2 Related Work

Multimodal Large Language Models. The domain of MLLMs has witnessed significant advances, particularly in the enhancement of visual and language processing. Current MLLM is usually a combination of visual encoders [; ; ; ; ; ], LLMs, and fusion modules. Innovations like Flamingo [] and OpenFlamingo [] have advanced visual representation by integrating a Perceiver Resampler with vision encoders. BLIP-2 [] and InstructBLIP [] employ Q-Former to connect the frozen LLM and vision encoder. InternVL [] trains huge ViT and QFormer to integrate visual modalities through a multi-stage training method. MiniGPT4 [] leverages both a Q-Former and a linear projector to bridge the gap between the vision module and LLM. Furthermore, LLaVA [] adopts a simple MLP projector to promote the alignment between the LLM and vision encoder. mPLUG-Owl [] introduces an approach that begins to finetune the vision encoder and align visual features, followed by tuning the LLM using LoRA []. Qwen-VL [] improves visual module resolution to 448, aiming to refine the model’s visual processing capabilities. Fuyu-8B [] directly projects image patches before integration with LLM. MM1 [] has conducted ablative studies on connector design choices, revealing that the modality adapter type is less critical than the number of visual tokens and the resolution. MiniGemini [] utilizes high-resolution visual tokens and high-quality data to narrow the performance gap with GPT-4 and Gemini. With the rapid advancements in open-source models, proprietary models such as GPT-4V/4o [; ], Gemini [; ], Qwen-VL-Plus/MAX [], and Claude3 [] have achieved outstanding results in evaluations and practical applications. In this work, owing to the simplicity of the LLaVA architecture, we adopt a framework similar to LLaVA to design our model.

Multilingual Multimodal Models. Recent years have witnessed rapid progress in the expansion of multimodal models to include a wider variety of languages. M^3P [] leverages English as a pivot and alternates between English-only vision-language pre-training and multilingual masked language modeling. In contrast, UC^2 [] translates English captions into various languages and uses images as the anchor. mCLIP [] enhances the CLIP model by aligning it with a multilingual text encoder through knowledge distillation. Thanks to the expansion of the overall capabilities of large language models [; ; ; ], their multilingual capacities have significantly improved. Integrating multilingual LLMs with visual abilities has increasingly become a research focus. In the domain of LLMs, PaLI [] develops a 17B multilingual language-image model that spans over 100 languages. Ying-VLM [] discovers that instruction tuning in English can extend its applicability to other languages. Ziya-Visual [] illustrates the translation of English image-text datasets into Chinese, using in-context learning for instruction-response generation. VisCPM [] introduces a training paradigm that fine-tunes the MLLM in a quasi-zero-shot manner based on a strong multilingual large language model. Despite these advancements, they are primarily confined to two languages or rely on the massive translated corpus. On the other hand, there is no suitable multilingual benchmark for MLLMs to evaluate the performance of multiple languages. There are also some multilingual research studies in other domains, such as multilingual machine translation [; ; ; ].

## 3 MMMB: A Massive Multilingual Multimodal Benchmark

In this section,
we first discuss the limitations of existing benchmarks and then present the characteristics that an ideal multilingual benchmark should possess. Furthermore, we design and construct a new benchmark and provide its corresponding evaluation strategy.

### 3.1 Limitations of Existing Benchmarks

There are several existing multilingual benchmarks (*e.g.*, Multi30K [], M3Exam [], MMBench [], and LLaVA-Bench [; ]) for MLLMs, but they have some limitations: 1) Outdated Benchmarks. Multi30k is designed for image-text retrieval tasks, and the performance has nearly reached the upper bound due to the relatively easy problems. 2) Non-Standardized Evaluations. Other benchmarks, like LLaVA-Bench, rely on evaluations using GPT-4. Dependence on GPT-4 as a de facto “Ground Truth” may hinder reproducibility. Meanwhile, since LLaVA uses a deprecated version (GPT-4-0314), using other different versions could result in unfair comparisons. On the other hand, because M3Exam does not offer consistent test samples across different languages, it cannot ensure whether poor performance is due to the problem’s difficulty or the model’s lack of multilingual capabilities. 3) Limited Languages. MMBench and LLaVA-Bench are limited in English and Chinese, which can not measure the multilingual capabilities across a broad spectrum.

Figure: Figure 2: Some bad cases for multilingual benchmark. Left: code reasoning is strongly related to English. Middle: logical reasoning is too challenging. Right: lack relevance between image and text.
Refer to caption: x3.png

### 3.2 Construction of the Multilingual Benchmark

To more suitably evaluate the multilingual capabilities of MLLMs, an ideal benchmark should exhibit the following characteristics:

1) Languages with Significant Differences. It should cover a diverse array of language families, selecting languages that are as distinct and non-repetitive as possible. This ensures a broad assessment of MLLMs’ ability to adapt across linguistic variances.

2) Problems with Medium Level of Difficulty. The problems should not be too difficult (*e.g.*, logical reasoning) because the aim is to assess the multilingual understanding, processing, and generating capabilities of MLLMs, not logical reasoning skills.

3) Tasks with Multilingual and Multimodal. As shown in Figure 2, data within datasets should not be strongly related to English (*e.g.*, code reasoning). It cannot be inherently transformed into multiple languages since they are composed of English words. Moreover, images should be an indispensable part when MLLMs answer the question. For instance, if given a map of the United States and asked to identify its capital, MLLMs only require the text-only ability to answer this question. Therefore, it is essential that questions highlight a significant correlation between images and texts.

4) Content Consistency across Languages. The goal of this benchmark is to evaluate the multilingual capabilities of MLLMs, and we aim to show the discrepancies across different languages fairly. For example, if English questions mainly focus on addition within one hundred while Chinese questions mainly concern calculus computation, it becomes difficult to ascertain whether poor performance in Chinese arises from the complexity of the problem or the limited multilingual capabilities of MLLMs. Hence, it is crucial to ensure content consistency across languages for a fair comparison.

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:
1) Since MMBench [] officially includes English and Chinese versions, we extend it to the other four languages.
2) For the massive multilingual multimodal benchmark, denoted as MMMB, we select and clean the suitable data from ScienceQA [], MME [], and SEED-Bench [] datasets with established guidelines. These datasets are then processed into a Visual Question Answering (VQA) format, resulting in a total of 12,000 samples across all six languages.

### 3.3 Evaluation Strategy

Since random guessing can lead to $\sim$25% Top-1 accuracy for 4-choice questions, potentially reducing the discernible performance differences between various MLLMs. Additionally, MLLMs may prefer to predict a certain choice among all given choices [], which further amplifies the bias in evaluation. To address these issues, we implement a circular validation strategy inspired by MMBench. Specifically, MMMB is adapted to the format of Yes/No questions, where each image is paired with two questions, demanding ‘Yes’ and ‘No’ answers, respectively. As shown in Figure 8, an answer is considered accurate only if both questions are answered correctly; failing either results in marking the entire instance as incorrect. This strategy ensures a more rigorous evaluation of MLLMs, reducing the likelihood of random guessing and promoting more validated comparisons across different models.

Figure: Figure 3: Overview of MMMB. It incorporates 6 languages, 15 categories, and 12,000 questions.
Refer to caption: x4.png

## 4 Methods

### 4.1 Preliminaries: Visual Instruction Tuning

A representative work in MLLMs is LLaVA [], which introduces a simple yet effective method for achieving alignment between the vision encoder and the pre-trained LLM. Specifically, for a given input image ${\bf X}_{\texttt{v}}$, LLaVA utilizes the pre-trained CLIP vision encoder ViT-L/14 [] to extract the visual features ${\bf Z}_{\texttt{v}}=g({\bf X}_{\texttt{v}})$. It then employs Vicuna [] as the LLM to obtain the textual embeddings ${\bf H}_{t}$. To align the vision encoder with the LLM, a projector in the form of a multi-layer perceptron (MLP) denoted as ${\bf W}$ is learned. This projector converts ${\bf Z}_{\texttt{v}}$ into language embedding tokens ${\bf H}_{\texttt{v}}$, effectively facilitating the integration of multimodal information within the LLM’s framework.

$$ ${\bf H}_{\texttt{v}}={\bf W}\cdot{\bf Z}_{\texttt{v}},~{}\text{with}~{}~{}{\bf Z }_{\texttt{v}}=g({\bf X}_{\texttt{v}}).$ (1) $$

Finally, we input ${\bf H}_{v}$ and ${\bf H}_{t}$ into LLM to generate the model’s responses. However, after the modality alignment training, LLaVA loses its ability to process in non-English languages.

### 4.2 Pilot Study

To address the challenge of multilingual erosion in MLLMs due to the dominance of English in image-text data, we hypothesize that there is an inherent mismatch between visual tokens ${\bf H}_{v}$ and textual tokens ${\bf H}_{t}$, which tends to bias them towards English semantics, making the model more likely to generate outputs in English. Specifically, the widely-used vision encoder of OpenAI-CLIP [] is pre-trained on a vast corpus of English-centric image-text pairs, resulting in visual representations more aligned with English.

To explore this phenomenon, we train two distinct models using the same data: one incorporating OpenAI-CLIP vision encoder and the other integrating Chinese-CLIP [] vision encoder. As shown in Figure 1, the model equipped with OpenAI-CLIP struggles to generate suitable outputs according to the Chinese inputs, whereas the model using Chinese-CLIP not only understands the queries but also generates appropriate outputs in Chinese.
Moreover, to further evaluate the model’s Chinese capability, we test it on Chinese datasets and observe improved performance: from 66.4 to 68.3 on MMBench-CN and from 62.4 to 66.1 on MMMB-zh when utilizing Chinese-CLIP.

### 4.3 Textual Guidance to Drive Visual Token Alignment

Due to the low-resource nature of non-English multimodal data (*e.g.*, lack of large-scale, high-quality image-text data), we need to use nearly the same amount of data as LLaVA to enhance the model’s multilingual capabilities. Furthermore, motivated by these interesting findings and aiming to align visual tokens with textual embeddings at the language level, we propose Parrot, a novel approach that leverages textual guidance to facilitate the multilingual alignment of visual features. Parrot enables the transition of English-biased visual features acquired through the OpenAI-CLIP to accommodate other languages. This approach ensures that it can provide language-specific visual tokens to LLM based on the multiple language inputs, thereby enhancing its multilingual capabilities.

Figure: Figure 4: 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.
Refer to caption: x5.png

First, we extract visual features through the vision encoder and transform them into language embedding tokens ${\bf H}_{v}$ using a projector. We obtain the embeddings ${\bf H}_{t}\in\mathbb{R}^{N\times C}$ derived from text inputs via the word embedding table. Subsequently, to convert the English-biased features into language-specific features using textual guidance, we employ a cross-modal cross-attention mechanism to obtain ${\bf H}_{v}^{\prime}\in\mathbb{R}^{C}$:

$$ ${\bf H}_{v}^{\prime}=\text{Attention}(\mathbf{Q},\mathbf{K},\mathbf{V})=\text{ Softmax}\left(\frac{{\bf H}_{v}^{\text{cls}}{\bf H}_{t}^{T}}{\sqrt{C}}\right){ \bf H}_{t},$ (2) $$

where $\mathbf{Q}$ equals the matrix ${\bf H}_{v}$, $\mathbf{K}$ and $\mathbf{V}$ are equivalent to ${\bf H}_{t}$. ${\bf H}_{v}^{\text{cls}}\in\mathbb{R}^{C}$ is the [CLS] token of ${\bf H}_{v}$. This process allows the visual features to be dynamically adjusted based on the multilingual inputs.

Since the projected language embedding tokens ${\bf H}_{v}$ are English-biased, we need to convert them to language-specific embeddings for different languages. To this end, we introduce a lightweight Mix-of-Experts (MoE) module, which includes a router and several language transformation experts. The router of MoE is a linear layer that generates a probability distribution over the set of experts $\mathcal{E}=[e_{1},e_{2},\cdots,e_{E}]$, effectively predicting the probability of selecting and activating each expert. Each expert is an MLP designed to convert English-biased embeddings into language-specific embeddings. The inputs to experts $\mathcal{E}$ is ${\bf H}_{v}$, and the outputs have the same dimensions as the inputs.

Subsequently, to obtain a normalized probability distribution for activating language-specific experts, ${\bf H}_{v}^{\prime}$ is fed as input to the router. The router network contains a linear layer that computes the normalized weight matrix using ${\bf H}_{v}^{\prime}$ for voting, producing $\mathcal{P}\in\mathbb{R}^{E}$:

$$ $\mathcal{P}=\text{Softmax}(\text{Linear}({\bf H}_{v}^{\prime})),$ (3) $$

which selects and activates the specific experts. Moreover, we process the English-biased embeddings ${\bf H}_{v}$ through the selected experts to convert them into language-specific visual representations:

$$ $\text{MoE}({\bf H}_{v})=\sum_{i=1}^{k}\mathcal{P}[i]\cdot\mathcal{E}({\bf H}_{ v})_{i}.$ (4) $$

This approach effectively aligns English-biased embeddings with multiple languages, ensuring a more accurate and comprehensive representation across different linguistic contexts.
To stabilize training and convert visual embeddings with less variance in visual-semantic information, ensuring the model performs well in tasks beyond the multilingual multimodal domain, we utilize MoE reweighting to obtain the final language-specific visual embeddings ${\bf G}_{v}$:

$$ ${\bf G}_{v}={\bf H}_{v}+\alpha\text{MoE}({\bf x}),$ (5) $$

where $\alpha$ is the trade-off parameter. In conclusion, we first fuse the visual and textual inputs via Eq. 2 to transform the visual embeddings with textual guidance. Moreover, the fused result is inputted into the MoE module to select and activate the most relevant language experts via Eq. 3 and then obtain the language-specific embeddings via Eq.  4. Lastly, we employ MoE reweighting to convert visual embeddings with less variance in original visual-semantic information 5. This approach enables us to endow the MLLM with multilingual capabilities using as little multilingual data as possible. Figure 4 illustrates the architecture, the detailed MoE module, and the training stages of Parrot.

**Table 1: Details on the Parrot’s training data, derived from publicly available datasets and our in-house multilingual data.**
| Training Stage | Datasets | Samples | Total |
| --- | --- | --- | --- |
| Stage 1 | LLaVA-1.5-pretrain [] | 558K | 1.2M |
| Laion-Caption^∗ [] | 12K |  |  |
| CC12M-Caption^∗ [] | 645K |  |  |
| Stage 2 | LLaVA-1.5-finetune [] | 665K | 793K |
| ShareGPT4V-zh^∗ [] | 71K |  |  |
| ShareGPT4V-pt^∗ [] | 14K |  |  |
| ShareGPT4V-ar^∗ [] | 12K |  |  |
| ShareGPT4V-tr^∗ [] | 17K |  |  |
| ShareGPT4V-ru^∗ [] | 14K |  |  |

### 4.4 Training Stage

Our goal is to utilize as little multilingual data as possible to enhance the multilingual capabilities of MLLMs.
The whole training procedure is divided into two distinct stages:

Stage 1: Modality Alignment. In this stage, we keep both the vision encoder and the LLM weights frozen, focusing solely on optimizing the projectors to align the visual features ${\bf H}_{v}$ with the pre-trained LLM word embedding. This stage can be likened to training a visual tokenizer that is compatible with the frozen LLM. To enhance the diversity of images, we extract a portion of data from LAION [] and CC12M [] datasets and construct the in-house caption data through GPT-4V.

Stage 2: Instruction Tuning for Multilingual Alignment. We still keep the vision encoder weights frozen while continuing to train the projector, MoE, and LLM. Due to the design of the MoE module, Parrot can rapidly learn to align visual representations across multiple languages by using a small amount of multilingual image-text data. As shown in Table 1, we only use nearly 10K training data for each language in stage 2. This approach is particularly beneficial given the scarcity of data resources in low-resource languages.

To address the challenge of limited data in non-English languages, we use a semi-automatic approach to acquire image-text data. Initially, we partition the ShareGPT4V dataset [] randomly for each language, extracting a selection of non-duplicate, non-parallel image-text data for training. Subsequently, we implement a translation and calibration scheme using GPT-4 to convert English texts into texts of other languages. Recognizing that this step may introduce noise and potential translation errors, we apply a manual calibration process to further fine-tune and clean the data, thereby obtaining high-quality multilingual image-text data.

This two-stage training approach ensures effective modality and multilingual alignment, even with limited Non-English data, aligning well with the realities of data scarcity in low-resource languages.

## 5 Experiments

In this section, we begin with an overview of the experimental framework, providing details on specific implementations, evaluation benchmarks, and MLLMs used for comparative evaluation. Following this, we conduct a comprehensive comparison of Parrot with the state-of-the-art approaches using multilingual benchmarks. Additionally, we compare Parrot with leading models across a range of multimodal tasks. Finally, this section concludes with ablation studies and visualization of multilingual cases, highlighting the exceptional ability of Parrot in handling multilingual tasks.

### 5.1 Experimental Setup

Implementation Details:
In this study, we configure Parrot with the pre-trained CLIP ViT-L/14 [] as the vision encoder and Qwen1.5-Chat [] as the backbone for LLM. The initial learning rates for the two stages are set at $1e^{-3}$ and $2e^{-5}$, respectively, with the batch size of 256 and 128. The entire training process is notably optimized to 21 hours on the 16$\times$A100 GPUs setup, attributed to the use of the relatively small training datasets. Additionally, BF16 and TF32 precision formats are employed to meticulously balance speed and accuracy throughout the training process. As defined in Eq. 4, we set the number of experts to six to match the number of languages. Each expert is an MLP composed of two linear layers with SiLU [] activation function. More details are shown in Table 5.

Evaluation Benchmark:
Our evaluation is divided into two parts: one evaluates the multilingual capabilities of MLLMs, while the other assesses its overall performance. The first evaluation is performed on two datasets: MMBench [] and a newly developed benchmark MMMB. For MMBench, we expand it to include six languages through translation via GPT-4, followed by manual verification. For MMMB, we construct it following the methodology described in Section 3. We present the accuracy for each language in Table 2. Furthermore, the second evaluation covers a wide broad range of multimodal tasks, such as MME [], MMStar [], ScienceQA [], RealWorldQA [] and SEED-Bench [], with performance reported using a radar chart in Figure 5(b).

Comparison Models:
For comprehensive comparisons, we select leading open-source models in MLLMs, including LLaVA-1.5 [], LLaVA-NeXT [], Qwen-VL [], Monkey [], VisualGLM [], VisCPM [], MiniGPT-4-v2 [], shareGPT4V [], InstructBLIP [], mPLUG-Owl2 [], Mini-Gemini []. Furthermore, we incorporate closed-source methods in our benchmarks, including GPT-4V [], Qwen-VL-MAX [], and Gemini Pro [], to demonstrate their remarkable performance. For the evaluation process, we employ the VLMEvalKit in OpenCompass [], ensuring consistent configuration settings across all methods to maintain fairness in comparison. For most of the mentioned methods, we directly use the VLMEvalKit implementation. Alternatively, we integrate other methods not officially provided into this framework to ensure consistency in evaluation.

### 5.2 Main Results

In this section, we present the results of the multilingual experiment in Table 2 and the overall experiment in Figure 5(b). According to Table 2, Parrot-14B achieves state-of-the-art (SOTA) performance in all languages on the MMBench benchmark and also achieves the SOTA performance in five languages on the MMMB benchmark, with English in the second place. The multilingual capabilities of Parrot-14B closely reach that of GPT-4V, demonstrating the exceptional ability of our proposed architecture. Notably, Parrot-7B also validates the SOTA performance on both benchmarks across all languages, even surpassing the LLaVA-NeXT-13B. Additionally, as shown in Figure 5(b), this evaluation aims to show that Parrot not only possesses excellent multilingual capabilities but also provides an overall understanding of Parrot’s capabilities in handling various complex multimodal tasks (*e.g.*, MME [], MMStar [], and SEED-Bench []). Additionally, as depicted in Figure 5(c), we visualize the expert distributions within the MoE. It is evident that the second expert is predominantly activated when using the Chinese prompt, indicating that different experts are utilized for various language prompts. In existing multilingual benchmarks, Parrot also demonstrates competitive performance while using less than 1% of the data compared to other multilingual MLLMs, as illustrated in Figure 6.

**Table 2: Accuracy performance comparison on multilingual benchmarks. We report all compared methods with VLMEvalKit []. The best and second results are shown in bold and underline, respectively.**
| Method | LLM | MMMB | MMBench |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| en | zh | pt | ar | tr | ru | en | zh | pt | ar | tr | ru |  |  |
| Open-source models |  |  |  |  |  |  |  |  |  |  |  |  |  |
| LLaVA-1.5 [] | Vicuna-v1.5-7B | 67.07 | 58.83 | 59.76 | 43.50 | 46.43 | 59.06 | 65.37 | 58.33 | 59.02 | 36.16 | 43.90 | 56.95 |
| LLaVA-1.5 [] | Vicuna-v1.5-13B | 69.76 | 62.86 | 60.76 | 45.49 | 54.44 | 62.69 | 68.98 | 63.23 | 62.97 | 46.56 | 53.17 | 61.59 |
| LLaVA-NeXT [] | Vicuna-v1.5-7B | 70.87 | 61.57 | 61.81 | 42.74 | 46.95 | 63.85 | 67.95 | 60.56 | 60.39 | 38.40 | 45.36 | 59.62 |
| LLaVA-NeXT [] | Vicuna-v1.5-13B | 74.44 | 67.19 | 63.21 | 45.36 | 53.09 | 68.24 | 70.87 | 64.51 | 64.08 | 45.36 | 52.92 | 61.85 |
| Qwen-VL [] | Qwen-7B | 52.63 | 36.37 | 38.65 | 36.54 | 37.42 | 40.70 | 42.26 | 22.25 | 25.08 | 18.72 | 26.37 | 28.17 |
| Qwen-VL-Chat [] | Qwen-7B | 56.02 | 57.77 | 46.37 | 43.04 | 41.05 | 48.65 | 54.29 | 56.52 | 43.12 | 35.73 | 39.17 | 42.86 |
| MiniGPT-4-v2 [] | LLaMA2-13B | 38.71 | 30.05 | 31.52 | 26.60 | 26.02 | 29.23 | 23.88 | 11.76 | 14.26 | 2.49 | 6.78 | 12.54 |
| ShareGPT4V [] | Vicuna-v1.5-7B | 69.24 | 60.23 | 60.29 | 43.57 | 45.26 | 61.23 | 69.59 | 61.6 | 59.62 | 37.37 | 43.38 | 59.45 |
| InstructBLIP [] | Vicuna-7B | 39.47 | 32.92 | 35.67 | 23.80 | 28.36 | 36.37 | 27.83 | 18.81 | 27.14 | 3.26 | 8.50 | 20.87 |
| mPLUG-Owl2 [] | LLaMA2-7B | 67.25 | 60.99 | 59.70 | 45.78 | 45.43 | 62.63 | 66.15 | 59.36 | 58.24 | 37.88 | 47.68 | 60.39 |
| Monkey [] | Qwen-VL-7B | 66.02 | 58.18 | 46.31 | 38.83 | 37.66 | 48.59 | 58.07 | 53.52 | 49.57 | 31.01 | 31.35 | 45.18 |
| Monkey-chat [] | Qwen-VL-7B | 71.63 | 66.54 | 60.35 | 48.77 | 46.31 | 58.59 | 70.79 | 65.72 | 65.03 | 46.90 | 48.10 | 59.36 |
| VisualGLM [] | ChatGLM-6B | 31.05 | 18.07 | 19.42 | 15.38 | 22.81 | 19.77 | 23.2 | 17.18 | 11.43 | 2.92 | 6.62 | 5.33 |
| VisCPM-Chat [] | CPM-Bee-10B | 53.10 | 47.54 | 28.19 | 26.90 | 26.78 | 26.84 | 45.88 | 46.39 | 15.81 | 1.46 | 9.19 | 1.20 |
| Parrot | Qwen1.5-7B | 70.00 | 68.13 | 67.31 | 62.69 | 58.01 | 66.26 | 70.70 | 70.36 | 65.12 | 57.82 | 58.43 | 64.00 |
| Parrot | Qwen1.5-14B | 73.92 | 71.64 | 69.82 | 68.13 | 64.33 | 70.18 | 74.40 | 72.25 | 69.16 | 66.15 | 64.52 | 69.33 |
| Closed-source models |  |  |  |  |  |  |  |  |  |  |  |  |  |
| GPT-4V [] | Private | 74.97 | 74.21 | 71.46 | 73.51 | 68.95 | 73.10 | 77.60 | 74.40 | 72.51 | 72.34 | 70.53 | 74.83 |
| Gemini Pro [] | Private | 75.03 | 71.87 | 70.64 | 69.94 | 69.59 | 72.69 | 73.63 | 72.08 | 70.27 | 61.08 | 69.76 | 70.45 |
| Qwen-VL-MAX [] | Private | 77.19 | 75.26 | 72.16 | 70.82 | 66.02 | 74.21 | 76.80 | 77.58 | 74.57 | 75.00 | 69.07 | 75.00 |

### 5.3 Ablation Study

Ablation study on each component.
We conduct an ablation experiment on the multilingual data and the MoE module. As shown in Figure 5(a), using multilingual data improves performance in each language. Moreover, the MoE module significantly improves performance, demonstrating the effectiveness of our proposed method.

Figure: (a) Ablation study.
Refer to caption: x6.png

Ablation study on different datasets.
As shown in Table 3, it is evident that the inclusion of different multilingual datasets continually improves performance on the MMBench benchmark, and all models with 7B parameters are used for this experiment. This highlights the robustness and scalability of our approach to handling multiple languages effectively.

**Table 3: Ablation study on different multilingual training datasets in MMBench benchmark. Models with 7B parameters are used for this ablation.**
| Dataset | English | Chinese | Portuguese | Arabic | Turkish | Russian |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LLaVA-1.5-finetune | 69.4 |  |  | 66.6 |  |  | 60.3 |  |  | 55.3 |  |  | 52.1 |  |  | 60.7 |  |  |
| + zh | 69.2 | -0.2 |  | 68.6 | +2.0 |  | 64.1 | +3.8 |  | 59.1 | +3.8 |  | 50.9 | -1.2 |  | 61.6 | +0.9 |  |
| + zh pt | 71.1 | +1.7 |  | 70.4 | +3.8 |  | 65.4 | +5.1 |  | 57.9 | +2.6 |  | 52.1 | +0.0 |  | 62.9 | +2.2 |  |
| + zh pt ar | 71.0 | +1.6 |  | 68.6 | +2.0 |  | 65.7 | +5.4 |  | 58.6 | +3.3 |  | 52.2 | +0.1 |  | 62.2 | +1.5 |  |
| + zh pt ar tr | 70.4 | +1.0 |  | 68.7 | +2.1 |  | 64.9 | +4.6 |  | 61.2 | +5.9 |  | 59.7 | +7.6 |  | 62.0 | +1.3 |  |
| + zh pt ar tr ru | 70.7 | +1.3 |  | 70.4 | +3.8 |  | 65.1 | +4.8 |  | 57.8 | +2.5 |  | 58.4 | +6.3 |  | 64.0 | +3.3 |  |

Ablation study on monolingual fine-tuning datasets.
The ablation study presented in Table LABEL:tab:ablation-mono evaluates the performance of different monolingual datasets added incrementally to the baseline dataset LLaVA-1.5-finetune.
It highlights the significant impact of adding different multilingual datasets to a baseline model. Each dataset incrementally improves performance in its respective language and, when combined, leads to overall enhanced performance across all evaluated languages. This indicates the robustness and effectiveness of the proposed method in handling multilingual data, making it a scalable solution for multilingual tasks.

### 5.4 Visualization of Multilingual Conversations

Figure: Figure 6: Multimodal conversation cases of Parrot in multiple languages.
Refer to caption: x9.png

To enhance the intuitive understanding of the Parrot’s multilingual capability, we prepare a comprehensive case study accompanied by illustrative visuals. For instance, as depicted in Figure 6, our framework demonstrates remarkable multilingual capabilities. This underscores the Parrot’s versatility in navigating different languages and presents its potential in bridging linguistic gaps across diverse domains. Through careful analysis and visualization, we aim to provide a deeper insight into the mechanism driving this capability, illustrating its practical implications and potential applications in real-world scenarios. This visualization serves as a strong indicator of the Parrot’s solid architecture and its exceptional ability to understand, process, and generate multiple languages with remarkable efficiency. More multilingual conversation cases are shown in Appendix E.

## 6 Conclusion

This paper addresses the critical challenge of enhancing the multilingual capabilities of MLLMs. We introduce Parrot, a novel method leveraging textual guidance to drive visual token alignment at the language level, thus enabling the transition of English-biased visual embeddings into language-specific ones using an MoE module. Extensive experiments conducted on a newly introduced Massive Multilingual Multimodal Benchmark (MMMB) across six languages demonstrate the state-of-the-art performance of Parrot compared to existing methods, particularly presenting remarkable improvements in Turkish and Arabic. Furthermore, our model exhibits competitive results across a wide range of diverse multimodal benchmarks, emphasizing its efficacy in addressing both multilingual and multimodal challenges.
Parrot not only advances the frontier of MLLMs but also underscores the importance of equitable access to technological benefits across linguistic and cultural diversities.

## Appendix A Training Datasets in LLaVA

In this section, we analyze the multilingual data in LLaVA []. From Table 4(b) and Figure 7, it is evident that during the pre-train stage, LLaVA solely utilizes multimodal image-text pairs data for training, comprising 558K of English data. During the SFT stage, both multimodal and text-only data are incorporated into the training process. Multilingual data appear only in the text-only dataset. Apart from English, the most prominent non-English data is Chinese, amounting to just 3.1K, constituting 0.25% of the total dataset. Therefore, it is evident that LLaVA’s datasets are English-centric and imbalanced. The specific language and abbreviation are as follows: English (en), Chinese (zh), Korean (ko), Spanish (es), French (fr), Japanese (ja), German (de), Portuguese (pt), Traditional Chinese (zh-tw), Italian (it).

**Table 4: The detailed information about LLaVA’s datasets.**
| Training Stage | Type | Total Size | English | Other Languages |
| --- | --- | --- | --- | --- |
| Stage 1 (Pre-train) | Multimodal | 558K | 558K | - |
| Text-only | - | - | - |  |
| Stage 2 (SFT) | Multimodal | 624K | 558K | - |
| Text-only | 41K | 31K | 10K |  |

Figure: Figure 7: The pie chart of LLaVA’s multilingual data.
Refer to caption: x11.png

## Appendix B Training Details

As shown in Table 5, we provide the training hyperparameters for Parrot. Throughout all stages of training, we consistently train for one epoch, with a batch size of 256 for the first stage and 128 for the second stage. We maintain an image resolution of 336x336 for all two stages and enable the gradient checkpoint mode for each training stage.

**Table 5: Training hyperparameters.**
| Config | Stage 1 | Stage 2 |
| --- | --- | --- |
| Experts | - | 6 |
| MLP expert network | 2 Linear layers with SiLU |  |
| Deepspeed | Zero2 | Zero3 |
| Image resolution | 336×336 |  |
| Image encoder | Clip-ViT-L/14-336 |  |
| Feature select layer | -2 |  |
| Image projector | 2 Linear layers with GeLU |  |
| Epoch | 1 |  |
| Optimizer | AdamW |  |
| Learning rate | 1e-3 | 2e-5 |
| Learning rate scheduler | Cosine |  |
| Weight decay | 0.0 |  |
| Text max length | 2048 |  |
| Batch size per GPU | 16 | 8 |
| GPU | 16 × A100-80G |  |
| Precision | Bf16 |  |
| Gradient checkpoint | True |  |

## Appendix C Extended Experiments

In this section, we further provide more experiments and ablation studies to validate the generality and capability of Parrot across various tasks. Additionally, we present more training details about Figure 1 to offer a clearer understanding for readers.

### C.1 Bilingual Evaluation on LLaVA-Bench

VisCPM [] extends the LLaVA-Bench dataset to the Chinese version for bilingual evaluation. To comprehensively compare Parrot with other multilingual models, we conduct experiments on this benchmark. Due to the deprecation of the GPT-4-0314 version by OpenAI, we test Parrot in LLaVA-Bench following the version of GPT-4-1106-preview for comparison. As shown in Table 6, Parrot not only demonstrates exceptional ability in the English version of this benchmark but also presents competitive performance in the Chinese version.

Notably, as shown in Table 7, VisCPM uses 140M English data and 1M Chinese data to train the model, while Qwen-VL-Chat uses 1.1B English data and 300M Chinese data, whereas Parrot only utilizes approximately 2M data in total. Despite using less than 1% of the training data, Parrot achieves remarkable performance in both the English and Chinese versions on LLaVA-Bench. Owing to the architecture we proposed, significant improvement in the model’s multilingual capability can be achieved with minimal data usage.

Figure: Figure 8: An example of circular evaluation strategy.
Refer to caption: x12.png

**Table 6: Experimental results on LLaVA Test Set accessed by GPT-4. Con: Conversation, DD: Detailed Description, CR: Complex Reasoning, AVG: the average score of three tasks. The best/second best results are marked in bold and underlined, respectively. The symbol ^∗ denotes that the data are judged following the version of GPT-4-1106-preview because the GPT-4-0314 version is deprecated by OpenAI.**
| Model | LLM<br>Backbone | English | Chinese |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Con | DD | CR | AVG | Con | DD | CR | AVG |  |  |  |
| English<br>Model | MiniGPT-4 | Vicuna-13B | 65.0 | 67.3 | 76.6 | 69.7 | - | - | - | - |
| InstructBLIP | Vicuna-13B | 81.9 | 68.0 | 91.2 | 80.5 | - | - | - | - |  |
| LLaVA | Vicuna-13B | 89.5 | 70.4 | 96.2 | 85.6 | - | - | - | - |  |
| En-Zh<br>Bilingual<br>Model | mPLUG-OWL | BLOOMZ-7B | 64.6 | 47.7 | 80.1 | 64.2 | 76.3 | 61.2 | 77.8 | 72.0 |
| VisualGLM | ChatGLM-6B | 62.4 | 63.0 | 80.6 | 68.7 | 76.6 | 87.8 | 83.6 | 82.7 |  |
| Qwen-VL-Chat | Qwen-7B | 82.4 | 76.9 | 91.9 | 83.8 | 82.3 | 93.4 | 89.5 | 88.2 |  |
| VisCPM-Balance | CPM-Bee-10B | 75.5 | 64.7 | 91.3 | 77.3 | 85.4 | 81.4 | 96.6 | 88.0 |  |
| Multilingual<br>Model | Parrot^∗ | Qwen1.5-7B | 82.5 | 71.0 | 89.3 | 81.1 | 82.1 | 88.6 | 92.3 | 87.7 |

**Table 7: Comparison of vision encoders, LLMs, and training data.**
| Model | vision encoder | LLM | Training Data |
| --- | --- | --- | --- |
| mPLUG-Owl | ViT-L/14 (0.3B) | BLOOMZ-7B | - |
| VisualGLM | Q-Former (1.6B) | ChatGLM-6B | English: 300M; Chinese 30M |
| Qwen-VL-Chat | ViT-bigG (1.9B) | Qwen-7B | English: 1.1B; Chinese: 300M |
| VisCPM | Muffin (0.7B) | CPM-Bee-10B | English: 140M; Chinese: 1M |
| Parrot | ViT-L/14 (0.3B) | Qwen1.5-Chat-7B | English: 1.8M; Chinese: 71K |

### C.2 Radar Charts on MMBench and MMMB

For a more intuitive demonstration of the multilingual capabilities of Parrot, we present radar charts for the multilingual MMBench and MMMB benchmarks. As depicted in Figure LABEL:figure:radar_mmbench and Figure LABEL:figure:radar_mmmb, our proposed method Parrotexhibits significantly better performance compared to other models.

### C.3 More Experimental Details about Different Backbones

In this section, we provide detailed information to explain Figure 1. Firstly, to ensure a fair comparison between the OpenAI-CLIP-based model and the Chinese-CLIP-based model, we train distinct models using the same training data as LLaVA, as shown in Table 4(a). The hyperparameters are listed in Table 5 without the MoE hyperparameters. As depicted in Figure 1, the OpenAI-CLIP-based model struggles to generate Chinese outputs when given Chinese prompts due to the English-centric training data. In contrast, despite the extremely scarce amount of Chinese training data, the Chinese-CLIP-based model naturally acquires zero-shot capability to understand, process, and generate Chinese texts. Furthermore, we compare both models on MMBench-CN and MMMB-zh to evaluate their Chinese capability. As shown in Table 8, the performance of the Chinese-CLIP-based model is significantly higher than that of the OpenAI-CLIP-based model. On the other hand, we empirically find that different LLMs have a significant impact on performance. Qwen [] demonstrates superior Chinese capability compared to Vicuna [], yet its English capability remains competitive.

**Table 8: The performance of different vision encoders and LLMs on MMBench and MMMB. MMB refers to MMBench. “En/en” represents the English version, and “CN/zh” represents the Chinese version.**
| Method | Vision encoder | LLM | MMB-EN | MMB-CN | MMMB-en | MMMB-zh |
| --- | --- | --- | --- | --- | --- | --- |
| LLaVA | OpenAI-CLIP ViT-L/14 | Vicuna 7B | 65.4 | 58.3 | 67.1 | 58.8 |
| LLaVA | OpenAI-CLIP ViT-L/14 | Qwen1.5-Chat 7B | 68.8 | 66.4 | 68.2 | 62.4 |
| LLaVA | Chinese-CLIP ViT-L/14 | Qwen1.5-Chat 7B | 68.1 | 68.3 | 67.6 | 66.1 |
| Parrot | OpenAI-CLIP ViT-L/14 | Qwen1.5-Chat 7B | 70.7 | 70.4 | 70.0 | 68.1 |

## Appendix D Broader Impact and Limitations

Broader Impact. Parrot leveraging MoE to enhance multilingual alignment presents a positive social impact by promoting linguistic diversity and inclusivity. To address the challenge of the imbalanced language data in SFT datasets and improve non-English visual tokens alignment, this approach contributes to breaking language barriers and facilitating cross-cultural communication, thereby fostering understanding and collaboration across diverse linguistic communities. Additionally, the creation of the Massive Multilingual Multimodal Benchmark (MMMB) fills a crucial gap in evaluating multilingual capabilities, enabling researchers to assess and improve upon models’ performance across different languages and cultures. However, it’s crucial to acknowledge potential negative social impacts, such as the risk of hallucination. This could potentially result in the model generating misleading or incorrect information, which is a common challenge observed in MLLMs.

Limitations. Despite advancements, MLLMs may still exhibit limitations in accurately understanding and responding to complex language-specific contexts, leading to misinformation or misinterpretation of multilingual inputs. On the other hand, due to the visual component of Parrot being based on CLIP, there are inherent limitations in its ability to process high-resolution images, resulting in the inability to recognize extremely detailed content in some images. Hence, enhancing Parrot’s ability to handle high-resolution processing will be part of future work.

## Appendix E More Visualization Results

In this section, we include additional visualization results between users’ questions and Parrot’s responses using multiple languages. These pictures are selected from LLaVA [] and CuMo []. As depicted in Figures LABEL:figure:case_en, LABEL:figure:case_zh, LABEL:figure:case_pt, LABEL:figure:case_ar, LABEL:figure:case_tr and LABEL:figure:case_ru, it is evident that Parrotpossesses superior multilingual capabilities for understanding, processing, and generating multilingual texts. In certain specific cases, Parrot may also experience hallucinations. As depicted in the upper case of Figure LABEL:figure:case_en, it misidentifies Xiaomi SU7 as a Porsche Taycan.

## Peer Reviews

confidence: 4
contribution: 4
initial_rating: 6
presentation: 4
questions: 1) At the first pretraining stage, is the MoE initialized with random parameters? If yes, how can we learn a good Projector under a random MoE?

2) An open question: What is the most real benefit for today's MLLMs? Recent MLLMs can be divided into two groups: 1) dataset-driven, these models adopt simple adaptor to map images into the language space (Qwen-vl, llava, gpt-4o, gemini Pro) and jointly train MLLMs on massive image-text data. 2) tokenization-based models, these models believe a good image tokenization can align the image and text well (Parrot, [1][2]). In my opinion, the simple structure and target datasets fine-tuning may have better robustness and improvement than small structure-based modifications.

[1] https://arxiv.org/abs/2408.05019
[2] https://arxiv.org/pdf/2405.01926
soundness: 3
strengths: 1) The problem that improves the multilingual abilities of MLLMs is an open and challenging problem. This paper develops a simple and efficient insight for the community.

2) This idea that uses MoE-based adapter is interesting. It can learn from the limited image-text pairs and show good performance empirically.

3) The collected new benchmark MMMB would be useful for subsequent research.

4) Extensive comparison and ablations show the efficiency of the proposed model.
summary: This paper aims at the multilingual issues in recent MLLMs and proposes a MoE-based alignment layer to this end. First, the authors find that existing MLLMs are not friendly to non-English queries and analyze this from the imbalanced training datasets. A simple solution would be to train a new adapter for each language, but this approach is not feasible due to the limited number of training pairs. This paper thus introduces Parrot, which trains a soft adapter under the MoE framework. To fully test the multilingual ability of MLLMs, this paper also releases a Massive Multilingual Multimodal Benchmark(MMMB). Results on MMMB and MMBench show that Parrot has a better multilingual alignment with limited training data.
weaknesses: 1) Parrot  depends on balanced datasets and does not consider unbalanced situations, which are the most common in practice. That is saying that Parrot may not satisfy the scaling law. When considering massive training pairs, unbalanced cases occur, which could lead to sub-optimal MoE learning, sticking in the same predicament as existing MLLMs.

2) Lack of a strong baseline. I wonder about the performance of a naive baseline where we first translate the question into English and then translate the English answer back to the target language.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. As mentioned in Weakness #1, will the in-house dataset be open-sourced? The construction process should be explained in more detail, particularly regarding noise control and data diversity.
2. As mentioned in Weakness #2.
3. Parrot’s training consists of two stages, largely following LLava’s approach. However, the inclusion of the MoE architecture raises questions about its integration in stage 1. Specifically, how are the MoE weights initialized? If initialized randomly, is it optimal to include the MoE in stage 1, given that this stage focuses on aligning multimodal features? Additionally, if the MoE is indeed included in stage 1, an ablation study on whether to freeze or not freeze the MoE module would be insightful.
soundness: 3
strengths: 1. High reusability—the proposed approach is simple and highly portable.
2. Extensive experimentation—the paper validates multilingual capability across multiple benchmarks.
3. Open-sourced multilingual benchmark dataset (MMMB) to support multilingual evaluation for MLLMs.
summary: This paper proposes Parrot, an MLLM designed to handle multilingual tasks. Parrot follows the LLava architecture, introducing an additional MoE module after the visual projector to enhance multilingual understanding. During training, Parrot translates public datasets into multiple languages and adopts a two-stage training scheme similar to LLava. To assess multilingual capabilities in MLLMs, the paper introduces MMMB (Massive Multilingual Multimodal Benchmark), encompassing 6 languages, 15 categories, and 12,000 questions. Parrot demonstrates strong performance on both MMBench and MMMB.
weaknesses: 1. The in-house dataset is not discussed in detail, such as whether it will be open-sourced, and whether the manual calibration process mentioned in lines 365-366 follows the same methodology as MMMB construction.
2. There is no experimental analysis on Parrot's performance loss in a single language, for example, whether the use of multilingual data and the MoE module reduces the model's English proficiency.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Please see the above weaknesses.
soundness: 3
strengths: - The author's observation regarding the lack of balance among different languages in the SFT data of MLLMs is insightful.
- The proposed method is simple and efficient.
- The benchmark proposed by the author is rigorous and highly relevant for evaluating multilingual MLLMs.
- The paper is well-written and easy to follow.
- The experiments are comprehensive, demonstrating the effectiveness of the method proposed by the author.
summary: This paper addresses the imbalance in the quantity of SFT data for different languages within training datasets used in MLLM's SFT process, which results in suboptimal alignment performance for various languages with limited data. To tackle this issue, the authors propose a novel structure that combines the cross-attention and MoE structure, enabling the input visual tokens to MLLM to be conditioned on the language input. Furthermore, to address the current lack of comprehensive benchmarks for multilingual multimodal tasks, the authors propose a new benchmark, named MMMB, which provides a more extensive evaluation for multilingual MLLMs. Extensive experiments validate the effectiveness of the proposed method on multilingual benchmarks.
weaknesses: - **Regarding the issue of alignment:** I agree with the author's observation that the imbalance of SFT data among different languages may lead to poor alignment between vision tokens and multilingual tokens in MLLMs. However, it should be noted that the alignment data in the pretraining phase consists of English-only data, and the amount of data in the pretraining phase is significantly larger than that in the SFT phase. Would the impact of alignment between visual tokens and different language tokens be more severe in the pretraining phase?
- **Lack of comparison for the latest MLLM models**: Some of the VLMs the author compares are outdated. Could the evaluation include the latest VLMs, such as Qwen2-VL [1] and LLaVA-OV [2]?
- **About the scalibility**: Introducing a language-aware structure is an effective approach, but if similar structures are not introduced, would simply increasing the proportion of different language data in the SFT data yield a similar improvement in model performance? In larger models, such as those with 30B or larger model, is the performance gain from this model design consistent?

[1] Peng Wang, et al. "Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution" arXiv preprint arXiv:2409.12191 (2024)

[2] Bo Li, et al. "LLaVA-OneVision: Easy Visual Task Transfer" arXiv preprint arXiv:2408.03326 (2024)

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: (1) Carefully study how the performance of language transformation experts affects the overall performance.        
(2) Inspect how the router works, like looking into the classification accuracy for different languages.         
(3) Compare with the baseline models fairly, like using the same training data.
soundness: 2
strengths: (1) The paper is clear and easy to follow.     
(2) The data imbalance problem in vision-language models is interesting.
summary: The paper aims to strengthen the multilingual ability of vision-language models.
Due to the data imbalance problem, vision-language models often perform better on English-based data while suffering from low performance on language with scarce data.

To address the problem, the paper proposes a routing strategy with textual guidance.
The router makes the image embeddings language-aware.
Moreover, the paper collects new data for non-English languages with the assistance of GPT-4.

Experiments on MMBench show reasonable improvements over baseline models, like LLaVA.
weaknesses: (1) How to train the language transformation experts for the MoE module?        
     Are there explicit constraints to optimize the routing?                
     As the paper mentions training the baseline LLaVA with the multilingual data suffers from data imbalance problem.                 
     However, the proposed training strategy can also suffer from the problem:                  
          I. The routing strategy can be dominated by English-based data.                        
          II. The language transformation experts should perform worse for languages with fewer data.                  
     The authors should discuss how the above two problems are addressed.  

(2) How does the MoE module affect model performance as the data size of each language increases?
     Also, how does the increased data size affect the baseline model performance, like LLaVA?

(3) The proposed method actually injects language information into the image embeddings, making $H_v$ language-aware.
      Another straightforward baseline is to train several translation expert models, translating other languages into English.

Meta-review: (a) Summary:
The paper introduces Parrot, a Mixture-of-Experts (MoE)-based model to improve multilingual alignment in Multimodal Large Language Models (MLLMs). It addresses imbalances in non-English data and proposes MMMB, a multilingual benchmark. Empirical results demonstrate Parrot’s improved performance across benchmarks compared to baselines.

(b) Strengths:
Introduction of a new evaluation benchmark (MMMB).

(c) Weaknesses:
1) Baseline comparisons lack rigor; fair comparisons across pre-trained models and hyperparameters are missing.
2) The MoE's effectiveness in low-resource scenarios is not fully resolved.

(d) Decision:
Reject. While the paper presents a well-motivated study, the limited novelty, incremental technical contributions, and incomplete baseline comparisons lead to rejection.

Decision: Reject