@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://aclanthology.org/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 {\textquotedblleft}zero-shot{\textquotedblright} 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."
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%0 Conference Proceedings
%T A Novel Multi-Step Prompt Approach for LLM-based Q&As on Banking Supervisory Regulation
%A Licari, Daniele
%A Benedetto, Canio
%A Bushipaka, Praveen
%A De Gregorio, Alessandro
%A De Leonardis, Marco
%A Cucinotta, Tommaso
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F licari-etal-2024-novel
%X 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.
%U https://aclanthology.org/2024.clicit-1.59/
%P 496-509
Markdown (Informal)
[A Novel Multi-Step Prompt Approach for LLM-based Q&As on Banking Supervisory Regulation](https://aclanthology.org/2024.clicit-1.59/) (Licari et al., CLiC-it 2024)
ACL