Large Model Mode Control for Multi-Strategy Inference
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Solution Overview
Problem
Existing technologies require multiple independent models for different inference strategies, leading to exponentially increasing training, deployment, and maintenance costs.
Innovation Solution
A large model is equipped with dedicated mode control identifiers for various predefined working modes, allowing it to automatically select and perform the appropriate inference strategy based on user input, thereby supporting multiple strategies efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple independent models are used for different inference strategies, then each model can be optimized for its specific strategy, but training, deployment, and maintenance costs increase exponentially
Solution Approach 1:
The patent merges multiple independent models into a single large model that can perform different inference strategies. Instead of maintaining separate models for each strategy, the system uses one unified model with multiple inference strategies embedded through mode control identifiers, thereby reducing the exponential growth of model management complexity while preserving the optimization benefits of having dedicated models for each strategy type
Solution Approach 2:
The large model is designed with universality to perform multiple inference strategies through mode control identifiers. The single model can be configured to execute different inference strategies (such as tree-of-thoughts, graph-of-thoughts, or other reasoning approaches) by receiving appropriate mode control identifiers as input, making it a multi-functional system that replaces multiple specialized models
2Reliability
If multiple independent models are used for different inference strategies, then each model can be specialized, but training and maintenance costs increase
Solution Approach 1:
The patent combines multiple specialized models into a single large model that can execute different inference strategies. This merging approach reduces the cumulative training and maintenance costs associated with managing multiple independent models while preserving the specialized capabilities needed for different inference strategies through the use of mode control identifiers
Solution Approach 2:
The large model achieves universality by being trained to perform multiple inference strategies. Instead of training and maintaining separate models for each strategy, the system trains one model to handle various strategies through mode control identifiers, significantly reducing the overall training and maintenance expenditure while maintaining specialized performance for each strategy type
3Adaptability or versatility
If a single large model supports multiple working modes with different inference strategies, then adaptability and generation efficiency are enhanced, but the model complexity increases
Solution Approach 1:
The patent introduces dynamic adaptability through mode control identifiers that allow the large model to switch between different inference strategies at runtime. The model structure remains relatively static, but its behavior dynamically adapts to different working modes through the input of mode control identifiers, enabling high versatility without proportionally increasing structural complexity
Solution Approach 2:
The mode control identifier acts as an intermediary mechanism between the user and the large model's inference strategies. Instead of the model needing complex internal switches or multiple architectures, the mode control identifier serves as a simple intermediary signal that triggers the appropriate inference strategy, thereby achieving high adaptability with minimal increase in model complexity
Data Source
AI summary
A large model-based information processing method, an apparatus, a device, and a medium are provided, which relate to the technical field of artificial intelligence, particularly to the technical fields of machine learning, deep learning, large models and the like. The method includes: obtaining a user input; determining a target working mode from a plurality of predefined working modes, where each predefined working mode has a corresponding inference strategy and is provided with a mode control identifier for triggering the inference strategy; and inputting the user input and the mode control identifier of the target working mode into the large model to obtain target output data generated by the large model based on the inference strategy of the target working mode.