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

The broad availability of large language models (LLMs) enables automation of time-consuming tasks and efficient generation of quality machine learning training datasets, as demonstrated by Matt Dzugan at Muck Rack.

While LLMs can solve many NLP tasks, they may not always be the most cost-effective or accurate option. Matt Dzugan showcased using a traditional NLP model trained on a suitable dataset to classify article topics efficiently, highlighting the importance of obtaining or creating high-quality training data.

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