Tommaso Cucinotta


2024

pdf bib
A Novel Multi-Step Prompt Approach for LLM-based Q&As on Banking Supervisory Regulation
Daniele Licari | Canio Benedetto | Praveen Bushipaka | Alessandro De Gregorio | Marco De Leonardis | Tommaso Cucinotta
Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)

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.

pdf bib
Noisy Neighbors: Efficient membership inference attacks against LLMs
Filippo Galli | Luca Melis | Tommaso Cucinotta
Proceedings of the Fifth Workshop on Privacy in Natural Language Processing

The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information. Regulatory measures like GDPR and CCPA call for using robust auditing tools to address potential privacy issues, with Membership Inference Attacks (MIA) being the primary method for assessing LLMs’ privacy risks. Differently from traditional MIA approaches, often requiring computationally intensive training of additional models, this paper introduces an efficient methodology that generates noisy neighbors for a target sample by adding stochastic noise in the embedding space, requiring operating the target model in inference mode only. Our findings demonstrate that this approach closely matches the effectiveness of employing shadow models, showing its usability in practical privacy auditing scenarios.