How to Improve LLM Factual Accuracy and Reliability

Explore techniques for improving the factual accuracy and reliability of large language models in this 29-minute talk by Matei Zaharia, Co-Founder and Chief Technologist at Databricks. Discover research-based approaches like the Demonstrate-Search-Predict (DSP) framework, which connects LLMs to factual information and enhances application performance over time. Learn about industry-focused solutions, including Databricks' development of "LLMOps" tools within the MLflow open-source framework. Gain insights into converting LLMs' text generation capabilities into dependable, production-grade applications for more truthful and accurate content generation.

How Can We Get LLMs To Tell The Truth?