Interactive Question Prompting for Accurate Machine Learning Tasks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Non-expert users face difficulties in efficiently and accurately instructing machine learning models to perform tasks due to a lack of expertise in providing the necessary content and instructions, leading to time-consuming modifications of the output.
Innovation Solution
A machine learning model generates task-specific question prompts and question-answer pairs to collect information from users, allowing non-experts to generate accurate task outputs by answering questions, with the model being trained using a general unsupervised process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-expert users provide instructions to machine learning models, then the model can execute tasks, but the users face difficulties in efficiently and accurately instructing the model due to lack of expertise
Solution Approach 1:
The patent introduces an intermediary system that includes a machine learning model generating question prompts and a processor that collects user answers. This intermediary structure bridges the gap between non-expert users and the machine learning model, enabling users to provide accurate instructions without requiring expertise in prompting techniques. The system translates user-friendly questions into effective model instructions.
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning model generates question prompts based on task requirements, collects answers from users, and uses these answers to generate accurate task outputs. This iterative feedback loop ensures that the model receives precise instructions while keeping the user interface simple and accessible to non-experts.
2Reliability
If users modify the output generated by the machine learning model, then the output can be improved to match user expectations, but the modification process is time consuming
Solution Approach 1:
The patent applies preliminary action by having the machine learning model generate question prompts before executing the task. These pre-generated questions guide the user to provide the necessary information in a structured manner, ensuring that the model receives accurate instructions from the outset. This eliminates the need for time-consuming iterative modifications later.
Solution Approach 2:
The patent replaces the traditional mechanical process of iterative output modification with an automated system that collects user answers through question prompts and directly generates accurate task outputs. This substitution eliminates manual revision steps while maintaining high accuracy in the final output.
3Measurement precision
If the machine learning model generates task-specific question prompts, then non-expert users can provide accurate information, but the system complexity increases
Solution Approach 1:
The patent implements universality by designing a multi-functional system where the machine learning model serves multiple purposes: generating question prompts, collecting user answers, and producing task outputs. This unified approach consolidates what would otherwise be separate complex components into a single cohesive system, reducing overall complexity while maintaining high measurement precision.
Solution Approach 2:
The system applies self-service by having the machine learning model automatically generate appropriate question prompts based on the task requirements without requiring manual configuration or complex setup. The model adapts to different tasks and generates suitable questions autonomously, simplifying the system while ensuring accurate information collection from non-expert users.
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.