Prompt Management Model Training Using Representative Task Data
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Solution Overview
Problem
Large language models require manual effort to craft appropriate prompts for efficient and accurate task performance, and without proper guidance, they can generate irrelevant or harmful responses.
Innovation Solution
A computer-implemented method trains a machine learning model to identify patterns in data, generate simple prompts, and prioritize words for tasks, using a training dataset to improve the model's ability to manage prompts effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prompt crafting is used for large language models, then task performance accuracy is improved, but manual effort and time consumption increase
Solution Approach 1:
The system enables large language models to automatically generate their own optimized prompts through self-training. The model learns to construct effective prompts by training on synthetic prompt-data pairs, eliminating the need for manual prompt engineering while maintaining high task performance accuracy
Solution Approach 2:
The system performs preliminary training to pre-process and structure prompt patterns before actual task execution. By pre-training the model on curated prompt examples and learning prompt construction patterns in advance, the system prepares the model to autonomously generate appropriate prompts without manual intervention during task execution
2Device complexity
If no prompt management is implemented, then system complexity is reduced, but response relevance and task accuracy deteriorate
Solution Approach 1:
The patent integrates prompt management functionality directly into the large language model through self-training, making the model multi-functional. The model simultaneously performs its primary language tasks and autonomous prompt construction, eliminating the need for separate prompt management systems while improving response relevance
Solution Approach 2:
The trained machine learning model acts as an intermediary between the user's task requirements and the large language model's response generation. The model learns to construct appropriate prompts that bridge the gap between user intent and model output, improving response relevance without adding complex external management systems
3Reliability
If extensive data and parameters are used for training, then model performance is improved, but training time and computational resources increase
Solution Approach 1:
The system uses partial action by focusing training on specific prompt construction patterns rather than comprehensive model retraining. The self-training approach targets only the prompt generation capabilities, using a curated subset of training data focused on prompt structures, thereby reducing overall training time while maintaining model performance
Solution Approach 2:
The training process is segmented into distinct phases: initial model training, synthetic prompt generation, and self-training on prompt-data pairs. This segmentation allows each phase to use optimized data and parameters specific to its goal, improving training efficiency while maintaining model performance through focused learning
Data Source
AI summary
A computer-implemented method for training a machine learning model for managing prompt. A processor set determines patterns of data in a sample dataset to identify representative data from the sample dataset. The processor set combines the representative data with context for a number of tasks to generate a number of simple prompts. Each simple prompt comprises a portion of the representative data and context for a task from the number of tasks. The processor set trains the machine learning model using a training dataset comprises the number of simple prompts. The machine learning model is trained to identify priorities of words in the number of simple prompts.