LLM Performance Prediction Using Feature-Based Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deploying Large Language Models (LLMs) requires significant computational resources and iterative fine-tuning, which is expensive and limits accessibility, necessitating a need for systems and methods to predict and optimize their performance efficiently.
Innovation Solution
A system that extracts performance features from LLMs, selects an appropriate AI-based prediction model, predicts performance, validates with actual metrics, identifies issues, and fine-tunes the model based on pre-stored rules to output optimized LLMs on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs are deployed into production environment with iterative fine-tuning and prompt refining, then prediction accuracy is improved, but computational resources and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-extracting features from performance data and pre-selecting appropriate prediction models before actual prediction is needed. The feature extraction module processes performance data from multiple data sources in advance, and the prediction model selection module identifies the best model based on extracted features, so that when prediction is required, the system can quickly proceed without repeating these time-consuming operations.
Solution Approach 2:
The system introduces intermediary components including a feature extraction module that acts as a mediator between performance data and prediction models, and a prediction model selection module that serves as an intermediary to choose the appropriate model. These intermediaries process and prepare data in advance, reducing the computational burden during actual prediction operations.
2Reliability
If LLMs undergo iterative fine-tuning and prompt refining to obtain accurate predictions, then model performance is improved, but development complexity and effort increase
Solution Approach 1:
The system segments the complex LLM development process into distinct modular components: a feature extraction module that handles data processing, a prediction model selection module that chooses appropriate models, and a performance prediction module that generates predictions. This segmentation allows each component to be optimized independently and simplifies the overall development process by dividing complex tasks into manageable units.
Solution Approach 2:
The system implements self-service mechanisms where the feature extraction module automatically processes performance data from multiple sources, the prediction model selection module autonomously selects appropriate models based on extracted features, and the system continuously validates predictions against actual performance metrics without requiring manual intervention for each step.
3Measurement precision
If significant computational resources are allocated for fine-tuning LLMs, then prediction accuracy is improved, but accessibility is limited due to expensive infrastructure requirements
Solution Approach 1:
The system creates a simplified copy or representation of the LLM prediction process that can be executed without requiring the full computational infrastructure of actual LLM fine-tuning. By extracting features and using pre-selected prediction models, the system generates accurate predictions using significantly fewer computational resources, making the technology accessible to organizations with limited infrastructure capabilities.
Solution Approach 2:
The system changes the operational parameters by switching from full LLM fine-tuning operations to a streamlined feature extraction and prediction model application approach. This parameter change reduces computational resource requirements while maintaining prediction accuracy, thereby improving accessibility without sacrificing performance.
Data Source
AI summary
Systems and methods for predicting performance of Large Language Models (LLMS) are disclosed. The system receives a performance data associated with at least one Large Language Model (LLM) from a plurality of data sources. The system extracts a plurality of features related to model performance from the received performance data. The system selects an appropriate Artificial Intelligence (AI)-based prediction model from among a plurality of Artificial Intelligence (AI)-based prediction models based on the extracted plurality of features. The system applies extracted plurality of features and the received performance data to selected appropriate Artificial Intelligence (AI)-based prediction model. The system predicts a performance of the at least one LLM based on results of the appropriate Artificial Intelligence (AI)-based prediction model. The system validates the predicted performance of the at least one LLM with actual performance metrics. The system determines at least one issue in model performance based on results of validation.