@inproceedings{warczynski-etal-2024-leveraging-large,
    title = "Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems",
    author = "Warczy{\'n}ski, J{\k{e}}drzej  and
      Lango, Mateusz  and
      Dusek, Ondrej",
    editor = "Mahamood, Saad  and
      Minh, Nguyen Le  and
      Ippolito, Daphne",
    booktitle = "Proceedings of the 17th International Natural Language Generation Conference",
    month = sep,
    year = "2024",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.inlg-main.48/",
    doi = "10.18653/v1/2024.inlg-main.48",
    pages = "622--630",
    abstract = "We introduce a simple approach that uses a large language model (LLM) to automatically implement a fully interpretable rule-based data-to-text system in pure Python. Experimental evaluation on the WebNLG dataset showed that such a constructed system produces text of better quality (according to the BLEU and BLEURT metrics) than the same LLM prompted to directly produce outputs, and produces fewer hallucinations than a BART language model fine-tuned on the same data. Furthermore, at runtime, the approach generates text in a fraction of the processing time required by neural approaches, using only a single CPU."
}