About Microsoft Kosmos-1
Language Is Not All You Need: Aligning Perception with Language Models” is a research paper that introduces KOSMOS-1, a Multimodal Large Language Model (MLLM). This model is capable of perceiving multimodal input, following instructions, and performing in-context learning for not only language tasks but also multimodal tasks. The goal of KOSMOS-1 is to align vision with large language models, advancing the trend of moving from LLMs to MLLMs.
Features of KOSMOS-1
- Multimodal Perception: KOSMOS-1 can perceive general modalities, enabling it to understand and process both text and images.
- In-Context Learning: The model is designed to learn in context, which means it can adapt to tasks with minimal examples, demonstrating few-shot learning capabilities.
- Instruction Following: KOSMOS-1 can follow instructions, showcasing its zero-shot learning capabilities.
- Web-Scale Multimodal Training: The model is trained on web-scale multimodal corpora, including text data, image-caption pairs, and arbitrarily interleaved images and texts.
- Support for Various Tasks: KOSMOS-1 natively supports language, perception-language, and vision tasks. This includes tasks like visual dialogue, visual explanation, visual question answering, image captioning, simple math equations, OCR, and zero-shot image classification with descriptions.
- Cross-Modal Transfer: The model can transfer knowledge from language to multimodal and vice versa, enhancing its understanding and performance on tasks.
- Raven IQ Test Benchmark: KOSMOS-1 is evaluated using an IQ test benchmark based on Raven’s Progressive Matrices. This test evaluates the nonverbal reasoning capabilities of MLLMs.
- Commonsense Reasoning: The model achieves better commonsense reasoning performance compared to traditional LLMs, indicating that cross-modal transfer aids in knowledge acquisition.
- General-Purpose Interface: KOSMOS-1 treats language models as a universal task layer, allowing it to handle various tasks and instructions in a unified manner.