Dynamic Inference-Time Parameter Selection for Generative Neural Networks
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Solution Overview
Problem
The setting of inference-time parameters for generative neural networks is typically done by the caller, requiring expertise and is inflexible, leading to suboptimal or incorrect outputs due to varying user intents and applications.
Innovation Solution
A method and system for dynamically determining inference-time parameters based on operational context information during the inference process, using a configurator device to set parameters for generative neural networks, such as Large Language Models, to optimize output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If inference-time parameters are set by the caller directly in the model call, then the system is simple and easy to operate, but the output quality is suboptimal due to lack of adaptability to varying user intents and applications
Solution Approach 1:
The patent introduces an intermediary component (inference-time parameter determination system) that sits between the caller and the generative neural network. This intermediary automatically determines optimal parameters based on operational context information, eliminating the need for callers to manually set parameters while ensuring high output quality through adaptive parameter selection.
Solution Approach 2:
The system enables self-service by allowing the inference-time parameter determination system to automatically select optimal parameters without requiring expert intervention from callers. The system uses operational context information to autonomously configure parameters, making the process both simple for users and reliable in output quality.
2Adaptability or versatility
If inference-time parameters are configured beforehand by the system or application, then the configuration is fixed and simple, but the system lacks flexibility to adapt to different user intents and applications
Solution Approach 1:
The patent implements dynamics by transitioning from static, pre-configured parameters to dynamic parameter determination. The system adjusts inference-time parameters in real-time based on operational context information specific to each inference request, enabling adaptability to different user intents and applications without requiring complex manual reconfiguration.
Solution Approach 2:
The system employs parameter changes by modifying inference-time parameters based on operational context information. Different parameter values are selected dynamically according to the specific inference request, allowing the system to adapt to varying requirements while maintaining manageable complexity through automated determination.
3Reliability
If expert knowledge is required to set appropriate inference-time parameters, then the output quality can be optimized, but the ease of operation decreases for non-experts
Solution Approach 1:
The system implements self-service by enabling automated determination of inference-time parameters using operational context information. This eliminates the need for expert knowledge while maintaining high output quality, as the system autonomously selects appropriate parameters based on the specific inference request and context.
Solution Approach 2:
The patent introduces an intermediary parameter determination system that bridges the gap between non-expert users and optimal parameter configuration. This intermediary automatically translates operational context information into appropriate parameter settings, preserving output quality while removing the barrier of expert knowledge requirements.
Data Source
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AI summary
Some embodiments are directed to a method for dynamic determination of inference-time parameters to control the stochastic generation process of a generative neural network. The method may include dynamically determining for an inference request, at least from operational context information, at least one of the inference-time parameters.