Prompt Generation for Task-Specific ML Without Fine-Tuning
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Solution Overview
Problem
General machine learning models require expensive and time-consuming training to perform specific tasks, limiting their timely application and innovative potential.
Innovation Solution
Generating prompts that simulate training and fine-tuned specialization of general ML models without the need for fine-tuning, using a system that includes a computing device, data stores, and a response engine to determine task objectives and generate prompts for generative models like GPT-3, enabling them to comprehend context and generate relevant outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general ML models are trained on specific training data sets to be effective in specific situations, then the model performance and reliability are improved, but the training process becomes expensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing prompts that simulate fine-tuned specialization for various tasks. These prompts are generated in advance and stored in a database, allowing the general ML model to access task-specific information without performing time-consuming training when actual tasks need to be executed.
Solution Approach 2:
The patent introduces prompts as intermediary elements between the general ML model and specific tasks. These prompts act as mediators that provide task-specific context and guidance, enabling the model to perform specialized functions without direct training on task data. The prompts bridge the gap between general model capabilities and specific application requirements.
2Reliability
If general ML models are trained on specific training data sets to be effective in specific situations, then the model reliability is improved, but the training cost increases
Solution Approach 1:
Instead of training the ML model on specific task data, the system creates copies of task information in the form of prompts. These prompts replicate the essence of fine-tuned specialization without requiring actual model retraining. The prompts are generated once and reused, significantly reducing the computational cost and energy consumption associated with training.
Solution Approach 2:
The system uses inexpensive, easily generated prompts as substitutes for expensive model training. These prompts are computationally cheap to generate and store, compared to the high cost of training data processing and model parameter updates. The prompts serve as a cost-effective way to provide task-specific guidance to the general model.
3Reliability
If general ML models are trained to perform specific tasks, then the task-specific performance is improved, but the timeliness of employing the model is reduced
Solution Approach 1:
The system performs preliminary generation of task-specific prompts in advance and stores them in a database. When a specific task needs to be executed, the relevant prompt is quickly retrieved and provided to the general ML model, enabling rapid deployment without waiting for time-consuming training processes to complete.
Solution Approach 2:
Prompts serve as intermediaries that enable the general ML model to quickly adapt to specific tasks without requiring retraining. The prompts are pre-computed and stored, allowing the model to access task-specific information instantly when needed, thus maintaining high timeliness in model deployment and task execution.
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for generating one or more prompts based on an input and the semantic context associated with the input. In examples, the prompts may be provided as input to one or more general ML models to provide a semantic context around the input and/or output of the model. The prompt simulates training and fine-tuned specialization of the general ML model without the need to use a fine-tuning process to actually train the general ML model into a fine-tuned state. Additionally, the model output may be evaluated for responsiveness to the input prior to being returned to the user. An advantage of the present disclosure is that it allows a general ML model to be applied to a plurality of applications without the need for expensive and time-consuming training to fine-tune the ML model.