M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
M$^3$IT dataset optimizes vision-language models with 40 datasets and 80 languages.
Key Findings
Methodology
The M$^3$IT dataset is constructed through four stages: manual instruction writing, data preprocessing, quality check, and dataset translation. It includes 40 tasks and 400 instructions, supporting 80 languages. The Ying-VLM model, developed in this study, combines the BLIP-2 vision encoder and Ziya-13B language model, achieving alignment between visual features and text embeddings through two-stage training.
Key Results
- Ying-VLM excels in knowledgeable VQA tasks, with a 3.2 ROUGE-L score improvement on OK-VQA.
- In Chinese vision-language tasks, Ying-VLM's ROUGE-L scores significantly outperform MiniGPT4 and InstructBLIP.
- In video-language tasks, Ying-VLM achieves a ROUGE-L score of 18.3 on MSRVTT-QA, surpassing BLIP-series baselines.
Significance
The M$^3$IT dataset provides extensive task and language coverage for multi-modal research, filling the gap of high-quality instruction datasets. The Ying-VLM model demonstrates strong generalization in complex tasks, advancing research and applications in vision-language models.
Technical Contribution
The M$^3$IT dataset offers a multilingual, multi-task instruction tuning framework, and Ying-VLM achieves outstanding performance across various tasks through alignment of visual features and text embeddings.
Novelty
M$^3$IT is the first to achieve large-scale multilingual instruction tuning in the vision-language domain, significantly expanding task and language coverage.
Limitations
- The translation quality of the dataset may affect model performance in certain languages.
- Ying-VLM was not trained with video inputs for video tasks.
Future Work
Future work could expand multilingual samples in M$^3$IT, improve translation quality, and explore model training with video inputs.
AI Executive Summary
Multi-modal multilingual instruction tuning is crucial for vision-language models, yet progress is hindered by the lack of high-quality datasets. The M$^3$IT dataset addresses this gap by integrating 40 tasks and 80 languages. The Ying-VLM model, combining BLIP-2 and Ziya-13B, achieves alignment between visual features and text embeddings through two-stage training, showing exceptional performance.
Experimental results indicate that Ying-VLM significantly outperforms existing baselines in knowledgeable VQA tasks and demonstrates strong generalization in Chinese and video-language tasks. The open availability of the M$^3$IT dataset will foster advancements in multi-modal research.
Nevertheless, there is room for improvement in translation quality and training for video tasks. Future research can enhance model performance by expanding multilingual samples and exploring training with video inputs.
Deep Analysis
Background
In recent years, instruction tuning has made significant progress in natural language processing, especially in large language models like ChatGPT. However, the development of vision-language models is limited by the lack of high-quality instruction datasets. Existing datasets are often monolingual with limited task coverage, failing to meet the needs of multi-modal research.
Core Problem
Current vision-language models lack high-quality instruction datasets that are multilingual and multi-task, limiting their generalization across different tasks and languages. Solving this issue is crucial for developing versatile intelligent assistants.
Innovation
The M$^3$IT dataset achieves a breakthrough in multi-modal multilingual instruction tuning by integrating 40 tasks and 80 languages. The Ying-VLM model demonstrates strong capabilities in complex tasks through alignment of visual features and text embeddings.
Methodology
- �� Manually write 400 task instructions
- �� Data preprocessing and quality check
- �� Translate key tasks into 80 languages
- �� Train Ying-VLM model with BLIP-2 and Ziya-13B in two stages
Experiments
Experiments use the M$^3$IT dataset for multi-modal instruction tuning, evaluating Ying-VLM's performance in knowledgeable VQA, Chinese vision-language, and video-language tasks. ROUGE-L is the primary evaluation metric.
Results
Ying-VLM excels in knowledgeable VQA tasks, with a 3.2 ROUGE-L score improvement on OK-VQA. In Chinese and video-language tasks, Ying-VLM significantly outperforms existing baselines.
Applications
The M$^3$IT dataset can be used to develop multilingual intelligent assistants, supporting research and applications in multi-modal tasks, especially in scenarios requiring complex reasoning and multilingual support.
Limitations & Outlook
The translation quality of the dataset may affect model performance in certain languages, and video task training did not use video inputs. Future work can enhance performance by expanding multilingual samples and exploring training with video inputs.
Plain Language Accessible to non-experts
Imagine you're at an international conference, and people ask you questions in different languages. The M$^3$IT dataset is like a universal translator, helping your intelligent assistant understand and answer these questions. It includes examples in many different languages and tasks, like a big dictionary. The Ying-VLM model is like a smart assistant that learns from these examples to give accurate answers in various situations. Although translations might not be perfect sometimes, it performs well in most cases.
ELI14 Explained like you're 14
Imagine you have a super smart robot friend that can understand 80 languages! The M$^3$IT dataset is like a super language textbook for this robot, with all sorts of tasks and questions. By learning from this content, the robot can give smart answers in different situations. Sometimes it might make small mistakes, but most of the time it helps you solve problems. In the future, it'll get even smarter!
Glossary
Instruction Tuning
Fine-tuning models with specific task descriptions to follow instructions and perform tasks.
Used to train large language models to improve task generalization.
Vision-Language Model
Models that process both visual and language information, supporting multi-modal tasks.
Ying-VLM is a model combining vision and language.
Multi-Modal
Involving multiple forms of information, such as images and text.
The M$^3$IT dataset supports research in multi-modal tasks.
ROUGE-L
A metric for evaluating the similarity between generated text and reference text.
Used to evaluate Ying-VLM's performance in VQA tasks.
Translation System
A system that converts text from one language to another.
Used to translate tasks in the M$^3$IT dataset into 80 languages.
Open Questions Unanswered questions from this research
- 1 How to improve dataset translation quality to enhance model performance in multilingual tasks?
- 2 How to effectively use video inputs for training in video tasks?
Applications
Immediate Applications
Multilingual Intelligent Assistant
Models trained with the M$^3$IT dataset can be used to develop intelligent assistants supporting multiple languages, helping users access information in different linguistic environments.
Vision-Language Research
Researchers can use the M$^3$IT dataset to study multi-modal tasks and explore applications combining visual and language information.
Long-term Vision
General Intelligent Systems
In the future, the M$^3$IT dataset can aid in developing general intelligent systems capable of adapting automatically in complex environments, supporting multilingual and multi-modal tasks.
Abstract
Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress in open vision-language models (VLMs) has been limited due to the scarcity of high-quality instruction datasets. To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions. Our M$^3$IT dataset comprises 40 carefully curated datasets, including 2.4 million instances and 400 manually written task instructions, reformatted into a vision-to-text structure. Key tasks are translated into 80 languages with an advanced translation system, ensuring broader accessibility. M$^3$IT surpasses previous datasets regarding task coverage, instruction number and instance scale. Moreover, we develop Ying-VLM, a VLM model trained on our M$^3$IT dataset, showcasing its potential to answer complex questions requiring world knowledge, generalize to unseen video tasks, and comprehend unseen instructions in Chinese. We have open-sourced the dataset to encourage further research.