Zobrazeno 1 - 10
of 2 439
pro vyhledávání: '"A, Nozza"'
Autor:
Yu, Zehui, Sen, Indira, Assenmacher, Dennis, Samory, Mattia, Fröhling, Leon, Dahn, Christina, Nozza, Debora, Wagner, Claudia
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evide
Externí odkaz:
http://arxiv.org/abs/2405.08562
Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing. This paper proposes FairBelief, a
Externí odkaz:
http://arxiv.org/abs/2402.17389
Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling fairnes
Externí odkaz:
http://arxiv.org/abs/2310.12127
Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-dom
Externí odkaz:
http://arxiv.org/abs/2309.02311
Large Language Models (LLMs) exhibit remarkable text classification capabilities, excelling in zero- and few-shot learning (ZSL and FSL) scenarios. However, since they are trained on different datasets, performance varies widely across tasks between
Externí odkaz:
http://arxiv.org/abs/2307.12973
As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun tran
Externí odkaz:
http://arxiv.org/abs/2305.16051
Autor:
Alessandro Mancon, Angelo Roberto Raccagni, Gloria Gagliardi, Davide Moschese, Alberto Rizzo, Andrea Giacomelli, Miriam Cutrera, Federica Salari, Fiorenza Bracchitta, Spinello Antinori, Andrea Gori, Giuliano Rizzardini, Antonella Castagna, Maria Rita Gismondo, Silvia Nozza, Davide Mileto
Publikováno v:
Emerging Microbes and Infections, Vol 13, Iss 1 (2024)
ABSTRACTMonkeypox virus (MPXV) infection confirmation needs reliable polymerase chain reaction (PCR) assays; in addition, viral clade attribution is a key factor in containment measures, considering a more severe syndrome in clade I and the possibili
Externí odkaz:
https://doaj.org/article/a1c79515d0914cfea3d062b26569ff12
Autor:
Camilla Satragno, Irene Schiavetti, Eugenia Cella, Federica Picichè, Laura Falcitano, Martina Resaz, Monica Truffelli, Stefano Caneva, Pietro Mattioli, Daniela Esposito, Alessio Ginulla, Claudio Scaffidi, Pietro Fiaschi, Alessandro D’Andrea, Andrea Bianconi, Gianluigi Zona, Laura Barletta, Luca Roccatagliata, Lucio Castellan, Silvia Morbelli, Matteo Bauckneht, Isabella Donegani, Paolo Nozza, Dario Arnaldi, Giulia Vidano, Flavio Gianelli, Salvina Barra, Elisa Bennicelli, Liliana Belgioia
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 49, Iss , Pp 100849- (2024)
Background: High-grade glioma (HGG) patients post-radiotherapy often face challenges distinguishing true tumor progression (TTP) from pseudoprogression (PsP). This study evaluates the effectiveness of systemic inflammatory markers and volume of enhan
Externí odkaz:
https://doaj.org/article/967fdd8b118e453ab4bbe5e61d5fd059
Autor:
Touileb, Samia, Nozza, Debora
Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected Sc
Externí odkaz:
http://arxiv.org/abs/2211.11678
Autor:
Bianchi, Federico, Kalluri, Pratyusha, Durmus, Esin, Ladhak, Faisal, Cheng, Myra, Nozza, Debora, Hashimoto, Tatsunori, Jurafsky, Dan, Zou, James, Caliskan, Aylin
Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day. We investigate the potential for these models to amplify dangerous and
Externí odkaz:
http://arxiv.org/abs/2211.03759