@inproceedings{licari-etal-2024-novel,
    title = "A Novel Multi-Step Prompt Approach for {LLM}-based {Q}{\&}As on Banking Supervisory Regulation",
    author = "Licari, Daniele  and
      Benedetto, Canio  and
      Bushipaka, Praveen  and
      De Gregorio, Alessandro  and
      De Leonardis, Marco  and
      Cucinotta, Tommaso",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/moar-dois/2024.clicit-1.59/",
    pages = "496--509",
    ISBN = "979-12-210-7060-6",
    abstract = "This paper investigates the use of large language models (LLMs) in analyzing and answering questions related to banking supervisory regulation concerning reporting obligations. We introduce a multi-step prompt construction method that enhances the context provided to the LLM, resulting in more precise and informative answers. This multi-step approach is compared with a standard ``zero-shot'' approach, which lacks context enrichment. To assess the quality of the generated responses, we utilize an LLM Evaluator. Our findings indicate that the multi-step approach significantly outperforms the zero-shot method, producing more comprehensive and accurate responses."
}