High-Entropy Query Element Mapping for Reliable Parameter Extraction
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Solution Overview
Problem
Machine learning models, particularly large language models (LLMs), are prone to inaccuracies and hallucinations when lacking sufficient context, leading to unreliable element extraction and mapping, which can cause errors in broader systems relying on these models.
Innovation Solution
A system using multiple machine learning models, including an entropy detection engine and an external inference engine, to determine and mitigate high entropy elements by flagging uncertain elements and performing probabilistic mapping to input parameters, ensuring accurate extraction and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine learning model is used for element extraction and mapping, then the system is simple and easy to operate, but the reliability and accuracy deteriorate due to hallucinations and insufficient context
Solution Approach 1:
The patent divides the machine learning system into multiple specialized models: an entropy detection engine that identifies uncertain elements, and external inference engines that handle high-entropy elements. This segmentation allows each model to specialize in specific tasks, improving overall reliability while managing complexity through functional decomposition.
Solution Approach 2:
The entropy detection engine acts as an intermediary between the query processing and final mapping. It detects high-entropy elements and routes them to appropriate external inference engines, preventing direct processing by models that would otherwise hallucinate or produce inaccurate results.
2Measurement precision
If multiple machine learning models are used to detect and process high entropy elements, then the accuracy and reliability improve, but the computational resources and processing time increase
Solution Approach 1:
The system applies multiple models selectively rather than universally. The entropy detection engine identifies only high-entropy elements that require additional processing, allowing partial application of external inference engines only where needed, thus reducing overall computational overhead while maintaining precision for critical elements.
Solution Approach 2:
Different parts of the query are processed with different levels of computational resources. High-entropy elements receive intensive processing from external inference engines, while low-entropy elements are handled by simpler models, optimizing resource allocation based on local needs of each query component.
3Manufacturing precision
If high entropy elements are processed with multiple models and probabilistic mapping, then the accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The entropy detection engine performs preliminary analysis to identify high-entropy elements before they are processed by external inference engines. This preliminary action allows the system to prepare and route only the necessary elements for intensive processing, reducing overall processing time by avoiding unnecessary computation on low-entropy elements.
Solution Approach 2:
The system applies complex probabilistic mapping only to high-entropy elements identified by the entropy detection engine, rather than processing all elements uniformly. This partial application of intensive processing methods reduces total processing time while maintaining high precision for the most uncertain and critical mappings.
Data Source
AI summary
Systems and techniques are provided for optimizing parameter extraction. For instance, a method for optimizing parameter extraction of queries is provided. The method can include extracting, from a query, a first element and a second element; determining an entropy level associated with the first element, the second element, and a plurality of input parameters; determining that the entropy level exceeds a threshold; based on the determination that the entropy level exceeds the threshold, determine a plurality of probabilities associated with mapping the first element and the second element to the plurality of input parameters; mapping, based on the plurality of probabilities, the first element to a first input parameter of the plurality of input parameters using a first machine learning model; and mapping, based on the plurality of probabilities, the second element to a second input parameter of the plurality of input parameters using a second machine learning model.