Efficient multi-task learning with instance selection for biomedical NLP.

Journal: Computers in biology and medicine
Published Date: Mar 31, 2025

Abstract

BACKGROUND: Biomedical natural language processing (NLP) increasingly relies on large language models and extensive datasets, presenting significant computational challenges.

Authors

  • Agnese Bonfigli
    Research Unit of Intelligent Technology for Health and Wellbeing, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, Rome, 00128, Italy; ItaliaNLP Lab, Institute of Computational Linguistics "Antonio Zampolli", National Research Council, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy.
  • Luca Bacco
    Unit of Computer Systems an Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.
  • Leandro Pecchia
    School of Engineering, University of Warwick, Coventry, CV4 7AL, UK. L.Pecchia@warwick.ac.uk.
  • Mario Merone
    Unit of Computer Systems an Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy. m.merone@unicampus.it.
  • Felice Dell'Orletta
    ItaliaNLP Lab, Institute of Computational Linguistics "Antonio Zampolli", National Research Council, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy.

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