The article discusses the low cost of embeddings in machine learning, exploring the factors that contribute to their affordability. It examines the technological advancements and efficiency improvements that have made creating and utilizing embeddings more accessible and economically viable for various applications.
Fine-tuned small language models (LLMs) can outperform larger models while being significantly more cost-effective, achieving results at 5 to 30 times lower costs. This efficiency is attributed to programmatic data curation techniques that enhance the training process of these smaller models.