4 citations · 4 across the 7 of their papers we have counts for
11 papers
Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech
Lorenzo Cima, Alessio Miaschi, Amaury Trujillo +3
AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue. Yet, existing approaches adopt a generic, o…
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
Andrea Pedrotti, Michele Papucci, Cristiano Ciaccio +4
Recent advancements in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for mali…
Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation
Luca Moroni, Giovanni Puccetti, Pere-Lluis Huguet Cabot +6
The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs c…
Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation
Lorenzo Cima, Alessio Miaschi, Amaury Trujillo +3
AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is o…
Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction
Quim Motger, Alessio Miaschi, Felice Dell'Orletta +2
Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews…
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models
Daniela Occhipinti, Michele Marchi, Irene Mondella +4
Automatic methods for generating and gathering linguistic data have proven effective for fine-tuning Language Models (LMs) in languages less resourced than English. Still, while th…