Papers by Andrea Favalli
Every time I fire a conversational designer, the performance of the dialogue system goes down (2022.lrec-1)
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Giancarlo Xompero, Michele Mastromattei, Samir Salman, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. |
| Approach: | They propose a system where domain scripts are coded in semi-logical rules and evaluated semi-logic rules produced by differently-skilled conversational designers. |
| Outcome: | The proposed system outperforms state-of-the-art systems when trained with smaller sets of annotated dialogues. |
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL translation (2024.findings-acl)
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Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati, Leonardo Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Large Language Models (LLMs) understand textual description to generate code in zero-shot scenarios, but there is a possibility that this ability may be influenced by having seen target textual descriptions and the related code. |
| Approach: | They propose a method to detect Data Contamination in Large Language Models (LLMs) and analyze their results on Termite and Spider Datasets to investigate their method. |
| Outcome: | The proposed method detects data contamination in GPTs and analyzes its performance on unfamiliar datasets. |
Position Paper: MeMo: Towards Language Models with Associative Memory Mechanisms (2025.findings-acl)
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Fabio Massimo Zanzotto, Elena Sofia Ruzzetti, Giancarlo A. Xompero, Leonardo Ranaldi, Davide Venditti, Federico Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli
| Challenge: | Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. |
| Approach: | They propose an architecture that explicitly memorizes sequences of tokens in layered associative memories. |
| Outcome: | The proposed architecture shows that memorization is a fundamental ability of large language models, achieved through learning. |