Integrating LLMs with Existing Text Analysis and Summarization Research Approaches

Oregon Convention Center 

Existing text analysis and summarization techniques like key term frequency analysis and unsupervised topic modeling are helpful for analyzing large quantities text but often are insufficient for contextual interpretations. We explore the groundbreaking integration of Large Language Models (LLMs) like GPT with these conventional techniques, highlighting this synergy through two real-world projects from distinct subject areas. This session offers a deep dive into the technicalities of using the GPT API in practice, comparing traditional text analysis methods with LLMs, various technological and methodological challenges, and work done to validate findings. We also discuss feedback and limitations of this approach in two real world settings with subject matter experts from non-technical backgrounds. We suggest further research opportunities for statisticians and sociologists and emphasize how LLMs can enhance analysis of large text datasets.

Text analysis

Large language models 

Section on Text Analysis