Deck Detail: Building Models that Learn from Themselves

Description: In his newsletter, Andrew Ng discusses the concept of enhancing large language models (LLMs) through agentic workflows and inexpensive token generation. He explains that while LLMs cannot significantly benefit from training on their own directly generated outputs, integrating an agentic workflow can produce higher-quality outputs suitable for use as training data. Ng compares this to a human learning from their own reflections and experiences. He highlights the costs associated with generating synthetic training data using various models and suggests that despite the high costs, the investment is feasible and can open up new opportunities for generating high-quality synthetic data.
Authors: Andrew Ng
Date Created: 5/8/2024
Last Updated: 5/8/2024
# Flashcards: 2
Tags: Machine Learning, AI Agents