@inproceedings{lango-dusek-2025-llm,
    title = "{LLM} Agents Implement an {NLG} System from Scratch: Building Interpretable Rule-Based {RDF}-to-Text Generators",
    author = "Lango, Mateusz  and
      Dusek, Ondrej",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.142/",
    pages = "2025--2040",
    ISBN = "979-8-89176-333-3",
    abstract = "We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is ``trained'' through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The resulting system is fully interpretable, requires no supervised training data, and generates text nearly instantaneously using only a single CPU. Our experiments on the WebNLG and OpenDialKG data show that outputs produced by our approach reduce hallucination, with only slight fluency penalties compared to finetuned or prompted language models."
}