@inproceedings{anderson-2024-prompting-assignment,
    title = "A Prompting Assignment for Exploring Pretrained {LLM}s",
    author = "Anderson, Carolyn Jane",
    editor = {Al-azzawi, Sana  and
      Biester, Laura  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Marasovi{\'c}, Ana  and
      Mathur, Leena  and
      Mieskes, Margot  and
      Weissweiler, Leonie},
    booktitle = "Proceedings of the Sixth Workshop on Teaching NLP",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.teachingnlp-1.12/",
    pages = "81--84",
    abstract = "As the scale of publicly-available large language models (LLMs) has increased, so has interest in few-shot prompting methods. This paper presents an assignment that asks students to explore three aspects of large language model capabilities (commonsense reasoning, factuality, and wordplay) with a prompt engineering focus. The assignment consists of three tasks designed to share a common programming framework, so that students can reuse and adapt code from earlier tasks. Two of the tasks also involve dataset construction: students are asked to construct a simple dataset for the wordplay task, and a more challenging dataset for the factuality task. In addition, the assignment includes reflection questions that ask students to think critically about what they observe."
}