Structured Inter-Model Communication for Zero-Shot Multimodal Tasks
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Solution Overview
Problem
Existing foundation models require substantial resources for re-training or fine-tuning to perform new tasks, and their capabilities are dependent on the distribution of training data across different domains.
Innovation Solution
Implementing structured inter-model communication schemas between pre-trained models to leverage their complementary zero-shot capabilities without additional training or fine-tuning, enabling the performance of new tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If foundation models are re-trained or fine-tuned to perform new tasks, then task-specific performance is improved, but computational resources and time required increase substantially
Solution Approach 1:
The system segments the task execution by dividing it into distinct phases: (1) retrieving relevant context data from storage, (2) processing the context data through pre-trained foundation models, and (3) generating responses based on model outputs. This segmentation allows the system to leverage pre-trained models without re-training, reducing computational resources while maintaining task-specific performance through context-aware processing.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing context data in a database before actual task execution. This allows the foundation models to operate on pre-organized data without requiring re-training, as the contextual information is already prepared and readily available for inference during task execution.
2Adaptability or versatility
If foundation models are trained on broad data at scale, then adaptability to various downstream tasks is improved, but the distribution of training data becomes considerably different across domains
Solution Approach 1:
The system introduces context data as an intermediary between the general foundation models and specific downstream tasks. The context data serves as a mediator that bridges the gap between broadly trained models and domain-specific requirements, allowing the models to adapt to different domains without re-training by retrieving and processing domain-relevant context information.
Solution Approach 2:
The foundation models maintain universality by being trained on broad, multi-domain data and capable of handling various downstream tasks through a single unified architecture. The system leverages this multi-functionality by retrieving relevant context data that enables the universal models to perform domain-specific tasks without requiring domain-specific re-training.
3Adaptability or versatility
If multiple pre-trained models are used to process multi-modal data, then task capabilities are enhanced, but system complexity increases
Solution Approach 1:
The system merges multiple pre-trained foundation models into a unified processing framework where different models can process different modalities of data simultaneously. By combining the capabilities of multiple models under a single system architecture with shared context data storage and retrieval mechanisms, the system enhances task capabilities while managing complexity through integration rather than separate independent systems.
Data Source
AI summary
Systems and methods of the present disclosure are directed to computer-implemented method for contextual processing via inter-model between pre-trained machine-learned models. The method includes obtaining, by a computing system comprising one or more computing devices, input data. The method includes processing, by the computing system, the input data with two or more pre-trained models to generate output data, wherein processing the input comprises executing a structured inter-model communication schema for inter-model communication between the two or more pre-trained models over a communications channel. The method includes providing, by the computing system, the output data as an output.