Large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks. The push toward humanlike artificial general intelligence (AGI) however will require equipping such models with additional capabilities — and multimodal perception is an essential next step.
In the new paper Language Is Not All You Need: Aligning Perception with Language Models, a Microsoft research team presents KOSMOS-1, a multimodal large language model (MLLM) that is able to perceive general modalities, learn in context, and follow instructions. KOSMOS-1 achieves impressive performance on language, perception-language, and vision tasks.
The researchers propose that LLMs with multimodal perception will be better equipped to acquire commonsense knowledge beyond the information they glean from text alone; and that this perception enrichment will facilitate LLM applications in new domains such as robotics and document intelligence. Multimodal perception also has the benefit of unifying multiple APIs to form a single general graphical user interface.
KOSMOS-1 follows the MetaLM training process, where a transformer-based LLM acts as a general-purpose interface and is augmented with various perception modules. Consistent with the MetaLM philosophy, the team treats language models as a universal task layer, enabling KOSMOS-1 to unify various task predictions as texts and capably handle natural-language instructions and action sequences.
Given a previous context, KOSMOS-1 learns to generate texts in an autoregressive manner. All non-text input modalities are embedded and then fed into its backbone transformer-based causal language model, with the transformer decoder serving as a general-purpose interface for all modalities. By interacting with natural language and the other modalities, KOSMOS-1 naturally inherits the capabilities of in-context learning and instruction following; and can thus handle both language and perception-intensive tasks.
In their empirical study, the team trained KOSMOS-1 on web-scale multimodal corpora and conducted experiments on a wide range of language and multimodal tasks and the Raven IQ test. KOSMOS-1 achieved impressive performance on all tasks, demonstrating its strong multimodal perception and nonverbal reasoning abilities.
In KOSMOS-1, the researchers introduce an MLLM with promising new capabilities and opportunities. In the future, they plan to equip KOSMOS-1 with speech and scale up its model size.
The paper Language Is Not All You Need: Aligning Perception with Language Models is on arXiv.
Author: Hecate He | Editor: Michael Sarazen
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The concept of KOSMOS-1 as a multimodal large language model is intriguing, especially its ability to align perception with language for more humanlike AGI. I appreciate that the researchers emphasize the role of commonsense knowledge acquisition through multimodal perception, which could indeed bridge the gap between text-only models and real-world understanding. The idea of unifying APIs into a single interface is also compelling, as it could simplify user interactions. However, I wonder about the scalability of such models in terms of training data diversity and computational resources. It would be interesting to see how KOSMOS-1 performs on less common modalities or in noisy environments. Overall, this work represents a significant step toward more holistic AI systems, and I look forward to future developments in this direction.
KOSMOS-1’s ability to unify multiple modalities through a single language model interface is a significant step toward AGI. The idea of using perception to enrich commonsense knowledge beyond text is intriguing, especially for applications like robotics and document intelligence. I’m curious how the model handles noisy or ambiguous visual inputs compared to text. Also, the MetaLM approach of treating language as a universal task layer could simplify complex multi-modal systems. Would love to see benchmarks on real-world tasks beyond standard datasets. Thanks for sharing this fascinating research.
The KOSMOS-1 model’s approach to multimodal perception is a significant stride towards AGI, as it allows LLMs to ground language in visual and other modalities, enhancing their ability to learn commonsense knowledge. The idea of using a single LLM as a universal interface for various tasks is compelling, as it simplifies deployment and improves generalization. I’m curious about the model’s performance on more complex reasoning tasks that require joint understanding of text and images, such as visual question answering with multiple steps. The potential applications in robotics and document intelligence are particularly exciting, as they could enable more natural human-machine interaction and automation. I look forward to seeing how this architecture evolves and whether it can scale to even broader modalities like audio and video.
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