@inproceedings{pasti-etal-2024-l,
    title = "An {L}* Algorithm for Deterministic Weighted Regular Languages",
    author = {Pasti, Clemente  and
      Karag{\"o}z, Talu  and
      Nowak, Franz  and
      Svete, Anej  and
      Boumasmoud, Reda  and
      Cotterell, Ryan},
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-main.468/",
    doi = "10.18653/v1/2024.emnlp-main.468",
    pages = "8197--8210",
    abstract = "Extracting finite state automata (FSAs) fromblack-box models offers a powerful approachto gaining interpretable insights into complexmodel behaviors. To support this pursuit, wepresent a weighted variant of Angluin`s (1987)L* algorithm for learning FSAs. We stay faithful to the original formulation, devising a wayto exactly learn deterministic weighted FSAswhose weights support division. Furthermore,we formulate the learning process in a mannerthat highlights the connection with FSA minimization, showing how L* directly learns aminimal automaton for the target language."
}