Part 5/13:
A significant part of the talk details an experiment involving an insurance company's email query resolution system. The goal was to assist agents in responding accurately to customer emails by interpreting intent, retrieving relevant data, and generating responses.
Initial Results: Using a base model with 53.5 million parameters, they achieved approximately 83% accuracy.
Synthetic Data Generation: The team utilized large language models (GPT-4, Anthropic, Mistral, etc.) to generate 7,000 synthetic samples covering diversified patterns, ensuring robustness.