MILS AI model sees and hears without training, GitHub code released

Facebook Research’s new MILS (Multimodal In-context Learning for LLMs) system represents a significant breakthrough in enabling large language models to process visual and audio information without dedicated training. By repurposing existing language capabilities, this inference-only method allows LLMs to interpret and generate content across multiple modalities, opening new possibilities for AI applications in image, audio, and video processing.

The big picture: Researchers from Meta have developed a technique that allows language models to “see” and “hear” without requiring additional training or fine-tuning.

• The system, called MILS, repurposes a language model’s existing capabilities to process visual and audio information through a novel inference approach.
• This breakthrough challenges the conventional wisdom that multimodal abilities require specialized architecture or extensive multimodal training.

How it works: MILS transforms visual and audio inputs into language-compatible formats that LLMs can process using their existing capabilities.

• The system converts images, videos, and audio into token sequences that language models can interpret as if they were reading text.
• During inference, MILS uses a technique that allows the model to iteratively refine its understanding of the multimodal content without any parameter updates.
• The entire process runs on a single A100 GPU, making it computationally efficient compared to many multimodal systems.

Key capabilities: The system enables multiple creative and analytical tasks across different media types.

• MILS can generate detailed captions for images, videos, and audio clips with high accuracy.
• The technology supports advanced image generation, enhancement, and style transfer operations.
• It can process and understand complex real-world visual scenes and audio content without specialized training.

Technical implementation: The code repository provides comprehensive instructions for installation and running various multimodal tasks.

• The system requires a specific conda environment and supports multiple datasets including MS-COCO, Clotho, and MSR-VTT.
• Different processing pipelines are available for image captioning, audio captioning, video captioning, image generation, and style transfer.
• The implementation allows batch processing through parallel execution to maximize efficiency.

Why this matters: This approach dramatically simplifies how AI systems can become multimodal, potentially accelerating development of versatile AI applications.

• By eliminating the need for specialized training or architecture modifications, MILS makes multimodal AI more accessible and efficient.
• The technique suggests language models already possess latent capabilities that can be unlocked through clever inference strategies rather than additional training.
• This could lead to more resource-efficient AI development by repurposing existing large language models for new modalities.

In plain English: This is like discovering that someone who only learned to read can suddenly understand spoken language and images just by thinking differently about the information, without having to learn new skills from scratch.