Generative Query Placeholder Filling for Missing Prompt Parameters
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Solution Overview
Problem
Existing generative models often require additional user inputs to provide detailed parameters for content generation tasks, leading to inefficient use of computational resources and prolonged human-computer interaction.
Innovation Solution
A generative model processes free-form natural language inputs to recognize missing parameters and insert placeholders, retrieving corresponding variable sets and probability values to generate a customized query, reducing the need for extensive training data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generative models process free-form natural language inputs directly, then the system is simple to operate, but computational efficiency deteriorates due to missing parameters requiring additional user inputs
Solution Approach 1:
The system performs preliminary processing by inserting placeholders for missing parameters into the initial query before the generative model execution. This preliminary action identifies and marks missing parameters (e.g., style, color) that need to be determined, allowing the system to prepare optimized queries in advance rather than requiring multiple iterative user inputs, thus improving computational efficiency while maintaining ease of operation
2Productivity
If generative models require detailed parameters for efficient task completion, then computational efficiency improves, but the complexity of user input increases
Solution Approach 1:
The system introduces an intermediary mechanism using placeholder tokens (e.g., #STYLE#, #COLOR#) that bridge the gap between simple user input and detailed model requirements. These placeholders act as mediators that automatically identify missing parameters without requiring users to manually provide detailed specifications, thus maintaining ease of operation while enabling computational efficiency through structured parameter handling
3Reliability
If multiple user inputs are required to provide complete parameters, then parameter completeness improves, but interaction time increases
Solution Approach 1:
The system implements a feedback mechanism where the initial query processing automatically identifies missing parameters and generates updated queries with placeholders. This feedback loop continues iteratively, with each cycle providing more complete parameter information, until the query is sufficiently detailed for efficient model execution. This automated feedback process ensures parameter completeness while minimizing interaction time by reducing the need for multiple manual user inputs
Data Source
AI summary
Implementations relate to receiving a free-form natural language input associated with a client device; processing, using a first generative model (GM), first GM input to generate corresponding first GM output; determining, based on the first GM output, an initial query that includes placeholder(s); retrieving placeholder data that includes, for the placeholder(s), a corresponding set of variables and a set of probability values corresponding to the set of variables; determining, based on the initial query, a final query; and providing the final query for processing by the first GM or a second GM. Determining the final query includes, for the placeholder(s): selecting, based on the corresponding set of variables and the set of probability values corresponding to the set of variables, a variable from the corresponding set of variables; and replacing the placeholder(s) with the selected variable.