LLM-Generated Simulator Code for Agile ML Application Training
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Solution Overview
Problem
Existing methods for generating applications using machine learning models require expensive and time-consuming simulations with conventional simulators, especially when specifications deviate, leading to increased costs and prolonged data generation times.
Innovation Solution
A system that generates applications using a large language model to interact with users, creating simulator code and parameters, simulating data, and training machine learning models without relying on existing simulators, enabling agile and cost-effective development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If an expensive existing simulator is used to perform the learning of the machine learning model, then the simulation data can be generated, but the cost is increased
Solution Approach 1:
The patent creates a virtual simulator through code generation based on natural language descriptions, copying the essential simulation functionality without requiring expensive physical simulator hardware. The generated simulator code replicates the needed simulation capabilities at minimal cost.
Solution Approach 2:
The patent generates simulator code that can be quickly created, modified, and discarded without significant cost. The simulator is built as disposable code artifacts that can be regenerated as needed, eliminating the need for expensive long-term simulator investments.
2Adaptability or versatility
If it is necessary to generate simulation data that greatly deviates from the existing specification of the simulator, then the learning of the machine learning model can be performed, but the cost associated with change of the specification increases and it takes a long time to generate the simulation data
Solution Approach 1:
The patent generates simulator code with dynamic specifications that can be easily adjusted according to the learning requirements. The simulator code incorporates configurable parameters that allow rapid adaptation to different simulation scenarios without time-consuming reconfiguration.
Solution Approach 2:
The patent changes simulation parameters directly in the generated code based on natural language descriptions. By modifying parameters such as environmental conditions, object properties, and simulation scenarios in the code, the system quickly generates diverse simulation data without lengthy reconfiguration processes.
3Reliability
If conventional simulators are used for application generation, then existing simulation capabilities are leveraged, but the device complexity and hardware resource burden increase
Solution Approach 1:
The patent extracts the essential simulation logic from complex conventional simulators and regenerates only the necessary components as simplified code. By taking out and regenerating only the essential simulation functionality rather than using complete conventional simulators, the system reduces overall complexity while maintaining required capabilities.
Solution Approach 2:
The patent creates simplified copies of simulation functionality through code generation rather than using full conventional simulators. The generated code replicates essential simulation behaviors with minimal complexity, copying only what is needed for the specific application rather than entire simulator systems.
Data Source
AI summary
An application generation device generates a request for a machine learning model used for an application newly generated, based on a result of a dialogue with a user of the application, the dialogue being conducted by using a large language model, generates code of a simulator and simulation parameter used for learning of the machine learning model based on the request by using the large language model, build and generate runtime of the simulator, generates simulation data which is a set of input to the machine learning model and output from the machine learning model, by using the runtime of the simulator, and generates the machine learning model by using the simulation data as learning data.