LLM-Labeled Neural Networks for Categorical Text Classification
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Solution Overview
Problem
Existing systems face challenges in categorizing large volumes of natural language text data due to the variability in human expression and the lack of high-quality labels for neural networks, leading to incomplete data analysis and missed insights in contexts like customer interactions and multimedia content.
Innovation Solution
Utilizing Large Language Models (LLMs) to identify concepts and generate labels, combined with clustering and neural networks trained on these labels to categorize concepts into categories and sub-categories, enabling comprehensive extraction and classification of intents, reasons, and actions in transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used in CRM tools, then data can be recorded, but data completeness and accuracy deteriorate due to human error and omission
Solution Approach 1:
The system enables self-service by allowing the LLM to automatically extract and categorize concepts from transcripts without manual intervention. The neural networks autonomously process the data, identifying intents, reasons, and actions while categorizing them into company-specific taxonomies, eliminating the need for manual data entry and improving both completeness and efficiency.
Solution Approach 2:
The patent replaces the mechanical system of manual data entry with an automated intelligent system. The LLM and neural networks substitute human agents in the data extraction process, using natural language processing to identify and categorize concepts from transcripts, thereby improving reliability while maintaining high productivity.
2Productivity
If neural networks are trained without high-quality labels, then training can proceed, but classification accuracy deteriorates due to lack of supervised learning signals
Solution Approach 1:
The system applies preliminary action by first using the LLM to generate high-quality labeled training data from transcripts before training the neural networks. The LLM extracts concepts and categorizes them into company-specific taxonomies, creating a supervised learning dataset that enables accurate classification while maintaining efficient training processes.
3Loss of information
If LLM is used to identify concepts from transcripts, then concept extraction completeness improves, but processing time increases due to analysis of entire transcript
Solution Approach 1:
The system applies segmentation by dividing the transcript analysis into distinct conceptual categories (intents, reasons, actions) that are extracted and processed separately. The LLM identifies these specific concept types from the transcript, and the neural networks further categorize them into company-specific taxonomies, enabling complete extraction while optimizing processing efficiency through structured segmentation.
4Adaptability or versatility
If company-specific categories are used for classification, then relevance to organizational needs improves, but system complexity increases due to custom taxonomy mapping
Solution Approach 1:
The system uses an intermediary approach by employing the LLM as a bridge between the transcript data and company-specific categories. The LLM generates initial concept extractions and categorizations, which are then mapped to the organization's custom taxonomy through neural networks. This intermediary processing layer simplifies the complexity of direct mapping while maintaining high organizational relevance.
Data Source
AI summary
The disclosure relates to systems and methods of identifying concepts in content having natural language text using a Large Language Model (LLM), training neural networks in a discovery phase to classify the identified concepts into categories, sub-categories, or other groupings of concepts, and executing the neural networks in an operational phase to classify identified concepts.