Job Script Validation Using LLM Extraction and Classifier Screening
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Solution Overview
Problem
The integration and operation of large language models at user facilities are challenging due to their size and cost, necessitating reliance on external services, which can be inconvenient and unreliable.
Innovation Solution
A data processing system comprising components that utilize a large language model to extract, analyze, and convert setting information from job scripts, employing classifiers and Bayesian models to determine inappropriate settings, and adjust priorities to ensure reliable execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large language model is incorporated and operated at user facilities, then the capacity and reliability of natural language processing is significantly increased, but the cost and complexity of the system increases
Solution Approach 1:
The system is divided into multiple functional components: a job script receiving component, a large language model component for extracting setting information, a classification component for determining appropriateness, and a job management component. This segmentation allows the complex LLM functionality to be distributed and integrated into existing infrastructure, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
The patent introduces an intermediary classification component that acts as a bridge between the large language model and the job execution system. This intermediary analyzes the setting information extracted by the LLM and determines whether it is appropriate before execution, thereby managing the complexity of integrating LLM into the existing system architecture.
2Measurement precision
If setting information is extracted and analyzed using large language model and classifier, then the accuracy of detecting inappropriate settings is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary extraction of setting information from job scripts using the large language model before job execution. By pre-analyzing and validating settings beforehand, the system ensures accurate detection of inappropriate configurations while allowing the actual job execution to proceed without delays caused during runtime validation.
Data Source
AI summary
A novel data processing system that is highly convenient, useful, or reliable is provided. The data processing system includes three components. A first component receives a job script, transmits the job script to a third component, and provides a determination result. A second component receives a first prompt, performs processing using a large language model, and extracts first setting information. The third component receives the job script and the first setting information, shares the job script and the first setting information in the third component, and transmits a determination result to the first component. The third component includes a first subcomponent and a second subcomponent. The first subcomponent creates the first prompt and transmits the first prompt to the second component. The second subcomponent performs processing using a classifier and determines whether the first setting information includes an inappropriate setting.