5 papers
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…
Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
Daniel Scalena, Sara Candussio, Luca Bortolussi +3
Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individual steps on the final answer poorly und…
EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
Daniel Scalena, Leonidas Zotos, Elisabetta Fersini +2
With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multip…
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…