Publication details

D. Licari, C. Benedetto, P. Bushipaka, A. De Gregorio, M. De Leonardis, T. Cucinotta. "A Novel Multi-Step Prompt Approach for LLM-based Q&As on Banking Supervisory Regulation," (to appear) in Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), December 4-6, 2024, Pisa, Italy.

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.

Last updated on
07 November 2024