Automating Text Analysis using Large Language Models

• In Event: From Raw Data to Interactive Results: AI-Assisted Data Workflows

Abstract

This tutorial will focus on retrieval-augmented generation (RAG) pipelines as a general approach to automated text analysis in the social sciences that harnesses their national language processing (NLP) abilities. It is intended to introduce social scientists to emerging methods for using artificial intelligence in text analysis research. The design, implementation, and evaluation of AI-automated workflows for research tasks such as text classification, qualitative coding, and data extraction will be explored. The promise of large language models (LLMs) to advancing social science research lies in researchers’ understanding of their probabilistic nature, the ability to control parameters, evaluate performance, and demonstrate the validity of results. These topics will be integrated into the discussion of creating a RAG pipeline and evaluating the validity of its outputs.

Author

• Zachary Courser, Claremont McKenna College
  Facilitator