This assignment, by Alan Knowles, is from the TextGenEd collection in the WAC Clearinghouse Repository.
The abstract from the site explains:
This assignment asks students to train a large language model (LLM) to generate Twitter posts in the style of specific accounts via a process known as few-shot learning, which trains the LLM on a small number of sample posts. Students use the trained LLM to generate tweets, then they rhetorically analyze the generated tweets. The assignment was originally developed for an entry-level Professional and Technical writing (PTW) course, but can be easily adapted to other disciplines and course levels.
Key Features of This Assignment
- Few-Shot Learning
- Students are tasked with training a large language model (LLM) using a small set of sample tweets to generate content in the style of specific Twitter accounts. This process, known as few-shot learning, introduces students to the intricacies of language model training and generation.
- Rhetorical Analysis
- After generating tweets with the trained LLM, students conduct a rhetorical analysis of the generated content. This analysis involves examining the language, style, and rhetorical strategies used by the LLM to mimic the target Twitter account.
- Adaptability and Application
- The assignment is designed for an entry-level Professional and Technical Writing (PTW) course but is easily adaptable to other disciplines and educational levels. It provides a practical application of LLMs in a real-world context, enhancing students’ understanding of both AI and rhetorical principles.
Find the full version of this assignment at the WAC Clearinghouse.