18296322. DISCOVERING AND APPLYING DESCRIPTIVE LABELS TO UNSTRUCTURED DATA simplified abstract (Microsoft Technology Licensing, LLC)

DISCOVERING AND APPLYING DESCRIPTIVE LABELS TO UNSTRUCTURED DATA

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Wolfgang Martin Pauli of Seattle WA (US)

Robert McArn Horton of Corte Madera CA (US)

DISCOVERING AND APPLYING DESCRIPTIVE LABELS TO UNSTRUCTURED DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18296322 titled 'DISCOVERING AND APPLYING DESCRIPTIVE LABELS TO UNSTRUCTURED DATA

Simplified Explanation

The patent application describes a method for training machine learning models using a combination of human-annotated samples and soft labels generated by a large language machine learning model.

Key Features and Innovation

• Selecting training samples from a dataset
• Generating soft labels using a large language machine learning model
• Training a student model with the training samples
• Evaluating the student model's performance based on human-annotated samples
• Selecting additional training samples using a teacher model
• Retraining the student model with the additional training samples

Potential Applications

This technology can be applied in various fields such as natural language processing, image recognition, and predictive analytics.

Problems Solved

This technology addresses the challenge of efficiently training machine learning models with limited human-annotated data.

Benefits

• Improved model performance
• Reduced reliance on human-annotated data
• Enhanced scalability of machine learning training processes

Commercial Applications

• Natural language processing applications in chatbots and virtual assistants
• Image recognition systems for security and surveillance
• Predictive analytics for personalized recommendations in e-commerce

Prior Art

Further research can be conducted in the areas of semi-supervised learning and active learning techniques in machine learning.

Frequently Updated Research

Stay updated on advancements in large language machine learning models and their impact on training machine learning models.

Questions about Training Machine Learning Models

How does this method improve the efficiency of training machine learning models?

This method leverages a combination of human-annotated samples and soft labels generated by a large language machine learning model to enhance the training process and improve model performance.

What are the potential applications of this technology beyond traditional machine learning tasks?

This technology can be applied in various fields such as natural language processing, image recognition, and predictive analytics, expanding its utility beyond conventional machine learning applications.

Original Abstract Submitted

Example solutions for training machine learning models include: selecting a plurality of training samples from a dataset; generating soft labels for the training samples using a large language machine learning model (LLM); training a student model using the plurality of training samples; evaluating a performance metric of the student model based on a plurality of human-annotated samples; selecting one or more additional training samples from the dataset using a teacher model; generating soft labels for the one or more additional training samples using the LLM; and retraining the student model using at least the plurality of training samples and the one or more additional training samples.