Prompt Parameter Evaluation Using Shapley Values for Quality Metrics
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Solution Overview
Problem
Existing methods for optimizing language model prompts are manual and resource-intensive, lacking the ability to objectively evaluate the contribution of various prompt parameters, leading to inefficient use of computing resources and subjective assessments.
Innovation Solution
Employing Shapley values to automate the evaluation of prompt generation parameters, determining their contribution to content quality metrics, and providing prompt parameter contribution metrics for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to optimize language model prompts, then subjective assessments can be performed, but the process becomes resource-intensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated computational system using Shapley values. The system automatically evaluates prompt parameters by computing their marginal contributions to content quality metrics, substituting human subjective assessment with objective mathematical calculation. This resolves the contradiction by maintaining assessment accuracy through rigorous computation while dramatically improving productivity through automation.
Solution Approach 2:
The system enables self-service evaluation where the prompt optimization process automatically assesses its own parameters without external human intervention. The Shapley value computation autonomously determines the contribution of each prompt parameter to content quality, allowing the system to serve itself in the optimization process. This eliminates resource-intensive manual involvement while preserving measurement precision through systematic computational evaluation.
2Loss of information
If comprehensive prompt parameter evaluation is performed, then contribution metrics can be determined, but computational costs and network latency increase
Solution Approach 1:
The patent applies partial action by computing Shapley values only for the specific prompt parameters that need evaluation rather than performing exhaustive analysis of all possible parameters. The system calculates marginal contributions for selected parameters individually, obtaining sufficient insight into parameter contributions without the excessive computational burden of evaluating every possible parameter combination. This resolves the contradiction by providing necessary information while controlling computational costs.
3Extent of automation
If automated Shapley value evaluation is implemented, then objective metrics are provided, but system complexity increases
Solution Approach 1:
The patent introduces Shapley values as an intermediary mathematical framework that bridges the gap between prompt parameters and content quality metrics. This intermediary mechanism systematically decomposes the complex evaluation process into manageable marginal contribution calculations for each parameter. By using this mathematical intermediary, the system achieves high automation while managing complexity through structured computation rather than unstructured system complexity.
Data Source
AI summary
Methods and systems are provided for using Shapley values to evaluate prompt generation parameters. In embodiments described herein, a selection of prompt parameters are accessed. A plurality of prompts are generated as a function of a combination of the prompt parameters. A corresponding quality metric is determined for each of the prompts. Prompt parameter contribution metrics are determined using a Shapley-value-based determination corresponding to a contribution of each of the prompt parameters to the corresponding content quality metric for each of the prompts. The prompt parameter contribution metrics are then displayed.