LLM Assistant for Supervised Learning Label Correction
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Solution Overview
Problem
Supervised machine learning systems face challenges in managing increasing volumes of incorrect classifications, requiring extensive human intervention for corrections, which is time-consuming and inefficient.
Innovation Solution
A system utilizing a large language model (LLM) engine to analyze incorrect classifications, generate preparatory work, and provide suggestions for human analysts, reducing the need for initial investigation and saving time by collating elements like target objects, feedback, and labeling rubrics to correct misclassifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supervised ML systems process increasing volumes of data and classifications, then classification capacity and throughput are improved, but the volume of incorrect classifications becomes increasingly difficult to manage and correct
Solution Approach 1:
The patent introduces an LLM-based assistant system as an intermediary between the supervised ML system and human experts. This assistant automatically generates preliminary correction suggestions for incorrect classifications, reducing the direct burden on human experts while maintaining correction quality. The LLM acts as a mediator that processes classification errors and provides structured correction recommendations.
Solution Approach 2:
The system enables partial self-service by allowing the ML system to automatically generate and implement certain types of corrections through the LLM assistant. The assistant can autonomously analyze incorrect classifications, generate correction suggestions, and apply corrections without requiring constant human intervention, thus reducing the management complexity of corrections at scale.
2Measurement precision
If human experts manually review and correct incorrect classifications, then correction accuracy is improved, but time consumption and operational inefficiency increase
Solution Approach 1:
The LLM assistant performs preliminary actions by automatically generating correction suggestions before human experts review the corrections. This preliminary analysis includes identifying incorrect classifications, proposing correction reasons, and preparing structured correction data, which significantly reduces the time human experts need to spend on each correction while maintaining accuracy through their final review.
Solution Approach 2:
The system implements a feedback loop where the LLM assistant's correction suggestions are reviewed and refined by human experts, whose corrections then feed back into training the LLM assistant. This continuous feedback mechanism improves the assistant's accuracy over time, reducing both time consumption and maintaining high correction quality.
3Reliability
If extensive human intervention is used for model and label corrections, then correction quality is improved, but operational efficiency and scalability deteriorate
Solution Approach 1:
The correction process is segmented into two distinct stages: automated preliminary correction suggestion generation by the LLM assistant, and human expert review and final approval. This segmentation allows the system to leverage the speed and scalability of automated AI processing while preserving human expertise for quality assurance, thus improving both efficiency and maintaining correction quality.
Solution Approach 2:
The LLM assistant is designed with multi-functionality, serving as both a classification error detector and a correction suggestion generator. It can handle various types of corrections including label corrections and model parameter adjustments, making it a universal tool that improves operational efficiency across different correction tasks while maintaining consistent quality standards.
Data Source
AI summary
A new approach is proposed to support efficient model and object labeling correction for supervised learning using large language models (LLMs). An LLM engine accepts and collates one or more of a plurality of elements of an incorrect classification/prediction/labeling of an object by a supervised learning system in order to complete preparatory work that a human analyst would perform upon receiving the incorrect classification of the object. Using these elements, the LLM engine analyzes and generates a suggestion/identification on how the plurality of elements are related. In some embodiments, the LLM engine annotates the document with the suggestion/identification and to generate a document in, for a non-limiting example, static HTML format, wherein the document can be inserted into a labeling interface for the human analyst to correct the labeling of the object and/or one more models used by the supervised learning system to classify the object.