Trial, Error, Triumph: Lessons Learned Using LLMs for Creating Machine Learning Training Data

The broad availability and performance of large language models (LLMs) enables practitioners to automate a variety of time-consuming tasks. Obtaining a large number of quality labels for a machine learning training dataset is a critical step in supervised learning, but can require prohibitive amounts of time to manually generate. At this year’s ODSC East, Matt Dzugan outlined an approach that his team at Muck Rack employs to generate high-quality machine learning training datasets using LLMs.

ai artificial intelligence automate availability data data science dataset error labels language language models large language large language models lessons learned llm llms machine machine learning performance quality supervised learning tasks training training data training dataset