An Interactive Toolkit for Approachable NLP

AriaRay Brown, Julius Steuer, Marius Mosbach, Dietrich Klakow


Abstract
We present a novel tool designed for teaching and interfacing the information-theoretic modeling abilities of large language models. The Surprisal Toolkit allows students from diverse linguistic and programming backgrounds to learn about measures of information theory and natural language processing (NLP) through an online interactive tool. In addition, the interface provides a valuable research mechanism for obtaining measures of surprisal. We implement the toolkit as part of a classroom tutorial in three different learning scenarios and discuss the overall receptive student feedback. We suggest this toolkit and similar applications as resourceful supplements to instruction in NLP topics, especially for the purpose of balancing conceptual understanding with technical instruction, grounding abstract topics, and engaging students with varying coding abilities.
Anthology ID:
2024.teachingnlp-1.17
Volume:
Proceedings of the Sixth Workshop on Teaching NLP
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Sana Al-azzawi, Laura Biester, György Kovács, Ana Marasović, Leena Mathur, Margot Mieskes, Leonie Weissweiler
Venues:
TeachingNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–127
Language:
URL:
https://aclanthology.org/2024.teachingnlp-1.17
DOI:
Bibkey:
Cite (ACL):
AriaRay Brown, Julius Steuer, Marius Mosbach, and Dietrich Klakow. 2024. An Interactive Toolkit for Approachable NLP. In Proceedings of the Sixth Workshop on Teaching NLP, pages 119–127, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
An Interactive Toolkit for Approachable NLP (Brown et al., TeachingNLP-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.teachingnlp-1.17.pdf