RESOURCE: AI for Humanists Tutorials

The collaborative, NEH-funded AI for Humanists project (formerly the BERT for Humanists project) creates learning resources aimed at empowering humanities scholars to use machine learning and artificial intelligence tools, specifically large language models (LLMs) in creative new ways.

Adding to its repository of tutorials, primarily for python coding, two new tutorials have recently been published on the project website:

  • Measuring Document Similarity with LLMs: “This code notebook demonstrates how you can use LLMs to explore which texts, or documents, are similar to each other in a given dataset. We explore narrative vs. non-narrative texts, historical poetry, and ChatGPT-generated poetry.”
  • Zero-shot Prompting with LLMs: “In this tutorial, we specifically explore how you can prompt a model to predict the genre of a book based on its Goodreads review and to predict whether a given passage is narrative or non-narrative text. But you should be able to use and modify this workflow for your own text classification needs.”


dh+lib Review

This post was produced through a cooperation between Mimosa Shah, Abbie Norris-Davidson, Kayla Abner, and Vera Zoricic (Editors-at-Large), Hillary Richardson and Rachel Starry (Editors for the week), Claudia Berger, Nickoal Eichmann-Kalwara, Linsey Ford, and Pamella Lach (dh+lib Review Editors), and John Russell (Editor in Chief).