Papers by Alberto Lavelli
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies (L18-1)
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Manuela Sanguinetti, Cristina Bosco, Alberto Lavelli, Alessandro Mazzei, Oronzo Antonelli, Fabio Tamburini
| Challenge: | Various approaches and ad hoc resources are needed to provide proper coverage of specific linguistic phenomena. |
| Approach: | They propose to annotate tweets using a well-known dependency-based annotation format . they propose to use the tweets for training NLP systems to improve their performance . |
| Outcome: | The proposed resource can be used for training of NLP systems on social media texts. |
Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning? (2026.acl-srw)
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| Challenge: | Adapting Large Language Models to the medical domain remains an active area of research . |
| Approach: | They propose to compare three common adaptation approaches to adapt large language models to the medical domain. |
| Outcome: | The proposed models are built on top of foundational LLMs and rely on different post-training methodologies for domain and task performance. |
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language (2020.lrec-1)
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| Challenge: | Using available datasets, we compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Approach: | They compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Outcome: | The proposed methods outperform traditional methods in sequence tagging tasks and classification tasks in Italian. |