LLM-Based Annotation Guidelines for Faster, More Accurate Data Labelling
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Solution Overview
Problem
Manual annotation processes for supervised machine learning are slow and prone to human errors, and existing methods for creating and updating annotation materials are inefficient.
Innovation Solution
A multi-agent system using Large Language Models (LLMs) for automatic guideline creation, improvement, and data annotation, incorporating a self-improvement mechanism and weakly supervised label models to enhance annotation accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to create labelled datasets, then annotation accuracy can be maintained through human review, but the annotation process becomes slow and labor-intensive
Solution Approach 1:
The patent introduces LLM-based agents as intermediaries between the annotation guidelines and the labelling process. These agents automatically interpret guidelines and apply them to data samples, serving as a mediator that bridges human-created guidelines and automated labelling, thereby maintaining accuracy while improving speed.
Solution Approach 2:
The system implements self-service through automated guideline creation and improvement mechanisms. LLM agents autonomously generate initial annotation guidelines, perform labelling tasks, and iteratively improve guidelines based on feedback from incorrect labellings, reducing the need for continuous human intervention while maintaining quality.
2Manufacturing precision
If annotation guidelines are manually created and updated, then guideline quality can be ensured through expert review, but the guideline creation process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by using LLM agents to automatically generate initial annotation guidelines before manual review. This pre-generation step provides a draft that experts can review and refine, significantly reducing the time required for guideline creation while maintaining quality through the expert review process.
Solution Approach 2:
The patent implements feedback loops where LLM agents automatically improve annotation guidelines based on performance feedback from labelling tasks. Incorrect labellings are used to identify guideline deficiencies, and the system iteratively refines guidelines, creating a continuous improvement cycle that reduces manual update time while maintaining quality.
3Measurement precision
If human annotators are used for data labelling, then label accuracy can be maintained through expertise, but the labelling process becomes slow and expensive
Solution Approach 1:
The patent replaces the mechanical system of human annotators with LLM-based agents that use natural language processing to perform labelling tasks. These agents process data samples by interpreting annotation guidelines and generating labels automatically, substituting human manual work with automated AI-based processing while maintaining accuracy through guideline adherence.
4Productivity
If existing annotation materials are used without updates, then the annotation process remains simple and fast, but the labelling accuracy deteriorates due to outdated guidelines
Solution Approach 1:
The patent implements dynamic guideline updates through LLM agents that continuously monitor labelling performance and automatically refine guidelines based on encountered edge cases and incorrect labellings. This dynamic adaptation allows the system to maintain high accuracy while preserving annotation efficiency, as updates occur automatically without disrupting the overall process.
Data Source
AI summary
A method may include: receiving a dataset comprising a plurality of label names and a label description for each label name; prompting a large language model (LLM) for initial annotation guidelines with the dataset; receiving, from the LLM, the initial annotation guidelines; receiving an unlabeled dataset comprising unlabeled text samples; predicting a label name for each unlabeled text sample; presenting each unlabeled text sample and its predicted label name for review; grouping incorrectly labeled text samples and their predicted label names; prompting the LLM for a recommendation to improve the initial annotation guidelines with the group of the incorrectly labeled text samples and their predicted label names and the initial annotation guidelines; receiving, from the LLM, the recommendation; prompting the LLM for improved annotation guidelines with the initial annotation guidelines and the recommendation; receiving the improved annotation guidelines; and deploying the improved annotation guidelines to a production environment.