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December 9, 2024
The development of multimodal large language models (MLLMs) has made rapid progress in recent years. These models, which can process both text and visual information, open up new possibilities in areas such as image captioning, visual question answering, and human-computer interaction. A key aspect for the performance of MLLMs is instruction tuning, where the models are trained to understand and execute instructions in natural language. A new research project now introduces MAmmoTH-VL, an MLLM trained through large-scale instruction tuning, achieving remarkable results in multimodal reasoning tasks.
Previous datasets for instruction tuning of MLLMs are often based on academic datasets such as VQA, AI2D, and ChartQA. These datasets mostly focus on simple tasks and only offer short answers without detailed explanations of the solution process. This limits the models' ability to draw more complex conclusions and make the reasoning process comprehensible. Another limiting factor is the size of existing datasets. Training powerful MLLMs requires large amounts of data to provide the models with sufficient examples and avoid overfitting.
The MAmmoTH-VL project addresses these challenges by developing a scalable and cost-efficient method for creating a comprehensive dataset for instruction tuning. This dataset contains detailed explanations of the reasoning process (Chain-of-Thought Reasoning) and covers a variety of reasoning tasks. In contrast to previous approaches, which often rely on human annotations or the distillation of GPT-4, MAmmoTH-VL exclusively uses open-source models for dataset creation. This makes the process significantly more efficient and cost-effective.
The resulting dataset comprises 12 million instruction-answer pairs and covers diverse, challenging reasoning tasks. The detailed explanations of the solution paths enable the model to draw complex conclusions and make the reasoning process transparent. This transparency is particularly important to strengthen trust in the results of AI systems and improve the interpretability of model predictions.
Experiments show that training MLLMs on this dataset significantly improves reasoning abilities. MAmmoTH-VL achieves state-of-the-art results on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%). Even on benchmarks not specifically designed for reasoning tasks, the model shows improvements of up to 4%. Ablation studies underline the importance of key components such as instruction rewriting and self-filtering in the dataset creation process.
The development of MAmmoTH-VL and its associated dataset represents an important step towards more powerful MLLMs. The scalable approach to dataset creation allows training the models with ever-larger and more diverse datasets, further improving their multimodal reasoning capabilities. This opens up new application possibilities for MLLMs in areas such as education, research, and creative applications. The combination of text and image understanding allows the models to process complex information and draw human-like conclusions – an important step on the path to truly intelligent Artificial Intelligence.
