Work

A Novel Multi-Step Prompt Approach for LLM-based Q&As on Banking Supervisory Regulation

Year: 2024

Type: conference-paper

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 standard "zero-shot" and "few-shot" approaches, 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.

Source: CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies)

Authors: Licari D., Benedetto C., Bushipaka P., De Gregorio A., De Leonardis M., Cucinotta T.

Institutions: Bank of Italy, Scuola Superiore Sant'Anna

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Banca d'Italia

OpenAlex ID: W7135157354

FWCI:

Cites: 26

Cited by:

Related to: 10