Dynamic Generative Model Selection for Latency and Accuracy Trade-offs
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Solution Overview
Problem
Current generative models, especially large language models, require significant computational resources and result in latency issues due to their large size, while smaller models are less robust and prone to generating inaccurate or under-specified responses.
Innovation Solution
Selecting between multiple generative models based on request features, server load, and computational efficiencies to dynamically choose a more efficient model that balances latency and accuracy, using a trained machine learning model to predict the probability of correct responses for each model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a larger generative model with more parameters is used, then response accuracy and robustness are improved, but computational resource consumption and latency increase
Solution Approach 1:
The system dynamically selects between multiple generative models of different sizes based on the specific request characteristics and current server load conditions. This dynamic adaptation allows the system to use larger models for complex requests requiring high accuracy while switching to smaller models for simpler requests, thereby resolving the contradiction between response accuracy and computational resource consumption.
Solution Approach 2:
The system changes the parameter of model size by maintaining multiple generative models with different parameter counts and selectively deploying them. This parameter variation enables the system to adjust the trade-off between accuracy and computational efficiency based on actual needs, rather than being fixed to a single model size.
2Productivity
If a smaller generative model with fewer parameters is used, then computational efficiency and latency are improved, but response accuracy and robustness decrease
Solution Approach 1:
The system dynamically adjusts model selection based on request complexity and server load, using smaller models for straightforward tasks to maximize computational efficiency while reserving larger models for complex tasks requiring higher accuracy. This dynamic approach resolves the contradiction by making efficiency gains without sacrificing overall system reliability.
Solution Approach 2:
The system segments the generative modeling task by creating multiple models of different sizes, each optimized for specific types of requests. This segmentation allows the system to match model capacity to task requirements, achieving high computational efficiency for simple tasks while maintaining accuracy capabilities for complex tasks through the segmented model architecture.
3Loss of time
If a smaller generative model is used, then processing speed and latency are reduced, but the risk of generating inaccurate or under-specified responses increases
Solution Approach 1:
The system dynamically selects models based on latency requirements and request characteristics. For time-sensitive requests or simpler tasks, smaller models reduce latency while maintaining adequate accuracy. For complex requests where accuracy is paramount, larger models are selected despite increased latency, thus dynamically resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system changes the parameter of model size to adjust the latency-accuracy trade-off. By having multiple models with different parameter counts available, the system can select the appropriate model size based on the specific latency constraints and accuracy requirements of each request, resolving the contradiction through parameter variation.
4Adaptability or versatility
If multiple generative models are maintained for dynamic selection, then system flexibility and optimization capability are improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by deploying multiple generative models that can handle different types of requests. This universal approach allows a single system to adapt to various task complexities and performance requirements, improving system flexibility while the models share common infrastructure and management mechanisms.
Solution Approach 2:
The system introduces an intermediary model selection mechanism that manages the complexity of multiple models. This intermediary layer handles model selection based on request characteristics and server load, shielding the user from the complexity of model management while enabling flexible adaptation. The intermediary resolves the contradiction by managing model diversity without exposing the full complexity to the user.
Data Source
AI summary
Implementations disclose selecting, in response to receiving a request and from among multiple candidate generative models (e.g., multiple candidate large language models (LLMs)) with differing computational efficiencies, a particular generative model to utilize in generating a response to the request. Those implementations reduce latency and/or conserve computational resource(s) through selection, for various requests, of a more computationally efficient generative model for utilization in lieu of a less computationally efficient generative model. Further, those implementations seek to achieve such benefits, through utilization of more computationally efficient generative models, while also still selectively utilizing less computationally efficient generative models for certain requests to mitigate occurrences of a generated response being inaccurate and/or under-specified. This, in turn, can mitigate occurrences of computational and/or network inefficiencies that result from a user issuing a follow-up request to cure the inaccuracies and/or under-specification of a generated response.