On Thursday, March 28, Sergio Correia joined Markus’ Academy for a conversation on “Unlocking Economic Data with LLMs.” Sergio Correia is an economist at the Board of Governors of the Federal Reserve System in the Division of Financial Stability.
Although barriers to entry of using Large Language Models (LLMs) appear high, in reality using these tools only requires a few lines of Python, and one need not be a professional coder to do so.
The talk covered simple examples that leverage LLMs to summarize publicly available text, extract historical data, or merge datasets. It also covered the basics of embeddings, and showed how these can be useful even without LLMs to classify text or run regressions (for example to predict Amazon product scores based on user reviews)
Lastly, the talk covered the nuances around designing a good Retrieval-Augmented Generation (RAG) pipeline to generate data with LLMs
All of the code from the talk’s examples can be reviewed here and here