@inproceedings{kasner-dusek-2026-animatedllm-explaining,
    title = "{A}nimated{LLM}: Explaining {LLM}s with Interactive Visualizations",
    author = "Kasner, Zden{\v{e}}k  and
      Dusek, Ondrej",
    editor = {A{\ss}enmacher, Matthias  and
      Biester, Laura  and
      Borg, Claudia  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Mieskes, Margot  and
      Serrano, Sofia},
    booktitle = "Proceedings of the Seventh Workshop on Teaching Natural Language Processing ({T}each{NLP} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.teachingnlp-1.1/",
    pages = "1--6",
    ISBN = "979-8-89176-375-3",
    abstract = "Large language models (LLMs) are becoming central to natural language processing education, yet materials showing their mechanics are sparse. We present AnimatedLLM, an interactive web application that provides step-by-step visualizations of a Transformer language model. AnimatedLLM runs entirely in the browser, using pre-computed traces of open LLMs applied on manually curated inputs. The application is available at https://animatedllm.github.io, both as a teaching aid and for self-educational purposes."
}