Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation

Autor: Ranaldi, Federico, Ruzzetti, Elena Sofia, Onorati, Dario, Ranaldi, Leonardo, Giannone, Cristina, Favalli, Andrea, Romagnoli, Raniero, Zanzotto, Fabio Massimo

Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is a severe possibility that this translation ability may be influenced

Externí odkaz: http://arxiv.org/abs/2402.08100

Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages

Autor: Ruzzetti, Elena Sofia, Ranaldi, Federico, Logozzo, Felicia, Mastromattei, Michele, Ranaldi, Leonardo, Zanzotto, Fabio Massimo

Publikováno v: Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, 2023, pages 14447 - 14461

The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language. In this paper, we propose a novel standpoint to investigate the above issue: using typological si

Externí odkaz: http://arxiv.org/abs/2305.02215

Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages

Autor: Ranaldi, Federico, Ruzzetti, Elena Sofia, Logozzo, Felicia, Mastromattei, Michele, Ranaldi, Leonardo, Zanzotto, Fabio Massimo

The overwhelming success of transformers is a real conundrum stimulating a compelling question: are these machines replicating some traditional linguistic models or discovering radically new theories? In this paper, we propose a novel standpoint to i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::adc4c684262065d4c045d2d5ac1a4b37
http://arxiv.org/abs/2305.02215