ML Intent Model Validation Using Differentiation Prompts
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Solution Overview
Problem
Existing machine learning models for interactive voice platforms face challenges in accurately inferring user intents due to the complexity of natural language processing, particularly when dealing with a large number of intents, leading to issues like human fatigue and limited variability in training prompts, which can result in suboptimal performance and user frustration.
Innovation Solution
A system and method that utilizes a generative artificial intelligence (GAI) to generate a diverse set of training prompts, combined with human feedback to refine and validate the models, comparing the performance of competing models using a reduced set of validation prompts to select the most accurate one.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are improved by iterative training and re-training on various data sets, then model performance is enhanced, but time consumption and computational resources increase
Solution Approach 1:
The system performs preliminary actions by generating a diverse set of validation prompts using LLMs before model comparison, and by pre-identifying differentiation prompts that will effectively distinguish between competing models. This preliminary preparation reduces the need for extensive iterative training by establishing a robust validation framework in advance.
Solution Approach 2:
The invention extracts only the essential differentiation prompts from the large set of validation prompts - specifically those prompts where models produce different responses. This extraction focuses computational resources on the most informative subset of data, reducing time consumption while maintaining model improvement effectiveness.
2Measurement precision
If a large set of validation prompts is used to test and compare ML models, then model evaluation accuracy is improved, but processing complexity and time increase
Solution Approach 1:
The system extracts and focuses only on differentiation prompts - a small subset of validation prompts where the competing ML models produce different responses. This extraction maintains high evaluation accuracy by concentrating on the most discriminative cases while dramatically reducing processing complexity compared to evaluating all validation prompts.
Solution Approach 2:
Instead of uniformly processing all validation prompts, the system applies local quality by identifying and prioritizing specific prompts with high discriminative value. The validation process adapts its focus to local regions of the prompt space where model differences are most apparent, improving efficiency without sacrificing overall evaluation accuracy.
3Manufacturing precision
If human analysts manually generate and validate training prompts, then prompt quality and model training effectiveness are improved, but labor requirements and costs increase
Solution Approach 1:
Large Language Models serve as intermediaries between automated prompt generation and human analyst validation. The LLMs generate diverse candidate prompts that are then reviewed and refined by human analysts, combining the scalability of automated generation with the quality control of human expertise. This intermediary approach reduces labor requirements compared to purely manual generation while maintaining high prompt quality.
Solution Approach 2:
Human analysts perform preliminary validation and guidance of prompt generation processes rather than complete manual creation. By establishing validation criteria and reviewing LLM-generated prompts in advance, human effort is optimized to focus on quality assurance and edge cases, reducing overall labor requirements while maintaining high prompt quality standards.
Data Source
AI summary
There is disclosed, in an example, a computer-implemented system and method, which includes providing a large set of validation prompts; testing a first ML intent model with the large set of validation prompts, wherein the first ML intent model is to select for respective validation prompts a first intent from an intent set; testing a second ML intent model with the large set of validation prompts, wherein the second ML intent model is to select for the same validation prompts a second intent from the intent set; selecting a reduced set of validation prompts, comprising validation prompts for which the first intent and second intent do not match; receiving an analysis of the reduced set of validation prompts, including indicia of hits, wherein one of the ML intent models inferred a correct intent; and selecting as a preferred model an ML model of the first ML intent model or second ML model that provided more hits.