Foundation Model Selection Using Contextual Utility Values
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Solution Overview
Problem
Enterprises face challenges in optimizing the use of diverse foundation models due to varying strengths and weaknesses, leading to inefficiencies and significant resource wastage in integrating these models into enterprise ecosystems, with no common standards for selection and management.
Innovation Solution
A method and system for selecting a set of foundation models based on contextual parameters, including functional requirement values and user preferences, to generate a foundation model pipeline that enhances utility and efficiency while meeting specific requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If foundation models are selected without systematic evaluation, then implementation is simpler, but model performance and efficiency deteriorate
Solution Approach 1:
The patent establishes a pre-computed utility value system that evaluates foundation models against contextual parameters before selection. Utility values are calculated in advance based on functional requirements and user preferences, enabling rapid model selection without repeated complex evaluations during actual deployment.
Solution Approach 2:
The patent introduces utility values as an intermediary metric that bridges the gap between complex model characteristics and selection requirements. Instead of directly comparing models on multiple dimensions, the system translates requirements into utility values, simplifying the selection process while maintaining comprehensive evaluation.
2Adaptability or versatility
If more foundation models are integrated into enterprise ecosystems, then functional capability improves, but resource wastage increases
Solution Approach 1:
The patent applies local quality by selecting foundation models with specific strengths matched to particular task requirements rather than uniformly deploying all models. Each model's unique capabilities are evaluated against contextual parameters to identify the most suitable models for specific functions, avoiding resource waste from deploying models with irrelevant strengths.
Solution Approach 2:
The patent changes the selection parameter from generic model popularity or size to contextualized utility values that reflect actual task requirements. By adjusting selection criteria based on functional requirements and user preferences, the system identifies optimal models that provide maximum capability with minimum resource consumption.
3Ease of operation
If foundation models are chosen based on popularity or size, then implementation is easier, but task-specific performance deteriorates
Solution Approach 1:
The patent fundamentally changes the selection parameter from superficial metrics like model size or popularity to contextualized utility values that directly reflect task-specific performance potential. Utility values are computed by evaluating model characteristics against functional requirements, providing a reliable indicator of actual performance without requiring complex manual assessment.
Solution Approach 2:
The patent replaces manual, subjective model selection processes with an automated utility value computation system. Instead of relying on human judgment or simple heuristics, the system automatically calculates and compares utility values based on quantitative model characteristics and contextual parameters, improving both ease of operation and performance reliability.
4Measurement precision
If comprehensive model evaluation is performed, then selection accuracy improves, but evaluation time increases
Solution Approach 1:
The patent performs comprehensive model evaluation in advance during the utility value computation phase, before models are deployed to production. By pre-computing utility values based on functional requirements and user preferences, the system eliminates the need for time-consuming evaluation during actual model selection or deployment.
Solution Approach 2:
The patent creates a simplified representation of complex model characteristics through utility values. Instead of requiring direct measurement of all model parameters during selection, the system uses pre-computed utility values that capture essential performance characteristics, significantly reducing evaluation time while maintaining selection accuracy.
Data Source
AI summary
Methods, systems, and computer-readable storage media for selecting foundation models. For selecting the foundation models, tasks and contextual parameters are obtained. The contextual parameters include functional requirement values and user preferences. The functional requirement values describe operating characteristics of a foundation model of a plurality of foundation models. Based on the functional requirement values and the user preference values, utility values of the foundation model are estimated. Based on the estimated utility values, a set of foundation models from the plurality of foundation models is selected. The functional requirement values and the user preference values constrain the selection of the foundation models. The selected set of foundation models are outputted for performing the tasks.