Guided Machine Learning Prompting With Task-Specific Questions
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Solution Overview
Problem
Non-expert users face difficulties in efficiently and accurately instructing machine learning models to perform tasks due to a lack of awareness of specific content or instructions needed, leading to time-consuming modifications of outputs.
Innovation Solution
A machine learning model generates task-specific question prompts and question-answer pairs to collect user information, allowing for the generation of customized outputs by extracting questions and replacing model-generated answers with user-provided answers, using a generative model trained through unsupervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-expert users provide basic instructions to machine learning models, then the ease of operation is improved, but the accuracy and completeness of task execution deteriorates
Solution Approach 1:
The system performs preliminary action by automatically generating comprehensive question prompts and multiple-choice options before the user provides input. The machine learning model pre-processes the task requirements and creates structured question-answer pairs that guide the user through necessary information provision, ensuring accurate and complete task execution without requiring expert user knowledge.
2Manufacturing precision
If users manually modify model outputs to achieve desired results, then the accuracy is improved, but the time consumption increases
Solution Approach 1:
The system implements feedback by presenting multiple-choice options generated by the machine learning model and allowing users to select from these pre-computed answers. This feedback mechanism eliminates the need for users to manually modify outputs, as the model provides refined answers directly through the interaction flow, significantly reducing time consumption while maintaining accuracy.
Solution Approach 2:
The machine learning model performs self-service by automatically generating question prompts, multiple-choice options, and refined answers without requiring user intervention for each step. The system serves itself by pre-processing task requirements and creating structured outputs that users can directly utilize, eliminating time-consuming manual modifications.
3Manufacturing precision
If the system generates multiple question prompts to collect user information, then the accuracy of task execution is improved, but the device complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex task execution into discrete question prompts with multiple-choice options. Each question prompt represents a specific information gap, and the segmented structure allows the machine learning model to systematically address each requirement independently, improving accuracy while keeping the interface simple and manageable.
Data Source
AI summary
Systems and methods relate to executing a task using a machine learning model based on prompt generation and collaborative interactions with a user. The machine language model generating a set of questions based on a task request. The user interactively answers the questions. A task processor generates a set of question-answer pairs based on the questions generated by the machine learning model and the answers given by the user. The machine learning model generates a task specific output based on the set of question-answer pairs. The machine learning model represents a large language model with deep learning. The simple question-and-answer prompts enable non-expert users to instruct the machine learning model with information that is sufficient to execute the task without overwhelming the users with the operations. The machine learning model leverages the answers to execute the task with accuracy, thereby providing efficacy of the prompting technique.