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🌐 Ziya-VL: Multi-Tasking Bilingual Vision & Language Model

Ziya-VL are open-source, bilingual, and optimized through instruction tuning and three-stage training on the BMMIC dataset.

🌐 Ziya-VL: Multi-Tasking Bilingual Vision & Language Model

Ziya-VL models fill the non-English gap in AI, excelling in multi-modal scenarios like image-text retrieval and captioning. They're open-source, bilingual, and optimized through instruction tuning and three-stage training on the BMMIC dataset.

Introduction and Problem Statement

The article opens by identifying a significant gap in the field of large language models (LLMs). While these models have shown remarkable capabilities in English, they are not as effective in non-English languages. The paper introduces Ziya-VL, a bilingual large-scale vision-language model designed to address this problem.

Ziya-VL Models and Their Components

The Ziya-VL series consists of two main models: Ziya-VL-Base and Ziya-VL-Chat. These models are built on the Querying Transformer architecture from BLIP-2. They are designed to incorporate visual semantics into large language models, making them suitable for multi-modal dialogues. The models use instruction tuning, multi-stage training, and a low-rank adaptation module to optimize visual-language alignment.

Optimization Techniques

The paper goes into detail about the optimization schemes used. Instruction tuning is a technique that helps the model understand and generate capabilities of visual information. Multi-stage training involves pre-training and two stages of instruction tuning to improve the model's performance. These techniques are crucial for aligning visual and textual data effectively.

BMMIC Dataset

A significant contribution of the paper is the introduction of the Bilingual Multi-Modal In-Context (BMMIC) dataset. This dataset is extensive, containing over 5 million image-text pairs in both English and Chinese. It serves as the foundational training data for the Ziya-VL models. The dataset is generated using GPT-4 for automated translation and generation of Chinese vision-language question-answer pairs.

Multi-Modal Scenarios and Performance

The Ziya-VL models are not just bilingual but also versatile. They show competitive performance in a wide range of tasks that require understanding both visual and textual data. These tasks include zero-shot image-text retrieval, image captioning, and visual question answering. The models are evaluated against existing large vision-language models and show promising results.

Bilingual Capabilities

One of the standout features of Ziya-VL is its bilingual nature. The models can understand and generate multi-modal dialogues in both English and Chinese. This is a significant step forward in making large language models more inclusive and effective across different languages.

Open-Source and Future Implications

The article concludes by emphasizing the open-source nature of the Ziya-VL models. The code, demo, and models are made publicly available, which is expected to encourage further research and development in the field of bilingual and multi-modal large language models.

To read more please check out: here. All credit for this research goes to the researcher of this project.

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