1 citations · 1 across the 6 of their papers we have counts for
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Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
Sara Candussio, Daniel Scalena, Luca Bortolussi +3
Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and hi…
Steering Large Language Models for Machine Translation Personalization
Daniel Scalena, Gabriele Sarti, Arianna Bisazza +2
Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, these systems still struggle when stylistic…
A gentle push funziona benissimo: making instructed models in Italian via contrastive activation steering
Daniel Scalena, Elisabetta Fersini, Malvina Nissim
Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources. A…
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
Daniel Scalena, Gabriele Sarti, Malvina Nissim +1
Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effecti…