mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2502.08468v1)

Abstract: Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space. However, the limited labeled multimodal data often hinders embedding performance. Recent approaches have leveraged data synthesis to address this problem, yet the quality of synthetic data remains a critical bottleneck. In this work, we identify three criteria for high-quality synthetic multimodal data. First, broad scope ensures that the generated data covers diverse tasks and modalities, making it applicable to various downstream scenarios. Second, robust cross-modal alignment makes different modalities semantically consistent. Third, high fidelity ensures that the synthetic data maintains realistic details to enhance its reliability. Guided by these principles, we synthesize datasets that: (1) cover a wide range of tasks, modality combinations, and languages, (2) are generated via a deep thinking process within a single pass of a multimodal LLM, and (3) incorporate real-world images with accurate and relevant texts, ensuring fidelity through self-evaluation and refinement. Leveraging these high-quality synthetic and labeled datasets, we train a multimodal multilingual E5 model mmE5. Extensive experiments demonstrate that mmE5 achieves state-of-the-art performance on the MMEB Benchmark and superior multilingual performance on the XTD benchmark. Our codes, datasets and models are released in https://github.com/haon-chen/mmE5.

• The paper introduces a synthetic data framework that enhances cross-modal alignment and fidelity between text and images.
• The model leverages diverse tasks such as classification and VQA, achieving superior results with 45 times less synthetic data usage.
• mmE5 demonstrates robust multilingual performance across 93 languages, outperforming previous models on MMEB and XTD benchmarks.

Overview of "mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data"

The paper "mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data" addresses the challenge of limited labeled multimodal data in the field of embedding models. These models map diverse data like text and images into unified representations. However, their efficiency is often hindered by the scarcity of labeled multimodal datasets, which are expensive to create. The research introduces an innovative approach that leverages high-quality synthetic data to enhance the performance of multimodal, multilingual embedding models, specifically the mmE5 model.

Synthetic Data Generation Framework

The authors identify three essential criteria for high-quality synthetic multimodal data: broad scope, robust cross-modal alignment, and high fidelity. The synthetic data should:

1. Cover a wide range of tasks and modalities.
2. Ensure semantic consistency across modalities.
3. Maintain realistic details for enhanced reliability.

To achieve these criteria, the authors have developed a novel data synthesis framework that incorporates a deep thinking process within a multimodal LLM (MLLM). This framework:

• Generates data applicable to various downstream tasks.
• Aligns different modalities semantically.
• Incorporates real-world images with contextually relevant text, ensuring fidelity through self-evaluation and refinement processes.

Model Training and Performance

Using both synthetic and available labeled datasets, the paper describes the training of a new multimodal multilingual E5 model, termed mmE5. The synthetic datasets cover a wide scope of tasks, ranging from classification and visual question answering (VQA) to cross-modal retrieval, and are multilingual, encompassing 93 languages. This dataset diversity ensures the model's generalizability across various contexts and scenarios.

The model achieves state-of-the-art results on the MMEB Benchmarks, significantly outperforming previous models like MMRet while using 45 times less synthetic data. Additionally, mmE5 exhibits superior multilingual capabilities, demonstrating enhanced performance on the XTD benchmark tasks.

Implications and Future Directions

This paper makes several contributions to the domain of multimodal multiligual embeddings:

• It sets a precedent for using high-quality synthetic data to overcome the limitations of data scarcity in model training.
• The framework for data synthesis can be applied to other models, potentially paving the way for improved multimodal and multilingual capabilities in future AI systems.
• By providing a comprehensive analysis of model performance across a broad range of tasks and languages, the paper highlights the crucial role of carefully crafted synthetic data in training robust models.

The implications for practical applications are profound, enabling more effective cross-modal and cross-linguistic AI systems with reduced dependence on costly human-labeled datasets. Future research could aim at further refining synthetic data quality, exploring additional modalities (such as audio), or examining scalability on larger datasets. Furthermore, the next steps could involve integrating diverse data generation techniques while considering the computational efficiencies and environmental impact of extensive synthetic data utilization.