@inproceedings{trebuna-dusek-2023-visuallm,
    title = "{V}isua{LLM}: Easy Web-based Visualization for Neural Language Generation",
    author = "Trebu{\v{n}}a, Franti{\v{s}}ek  and
      Dusek, Ondrej",
    editor = "Keet, C. Maria  and
      Lee, Hung-Yi  and
      Zarrie{\ss}, Sina",
    booktitle = "Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.inlg-demos.3/",
    pages = "6--8",
    abstract = "VisuaLLM is a Python library that enables interactive visualization of common tasks in natural language generation with pretrained language models (using HuggingFace{'}s model API), with tight integration of benchmark datasets and fine-grained generation control. The system runs as a local generation backend server and features a web-based frontend, allowing simple interface configuration by minimal Python code. The currently implemented views include data visualization, next-token prediction with probability distributions, and decoding parameter control, with simple extension to additional tasks."
}