18 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…
"Don't Say It!": Constraints, Compliance, and Communication when Language Models Play Taboo
Sara Candussio, Francesca Padovani, Daniel Scalena +1
The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple task combines strict lexical…
Corpus Prevalence of Multiple-Choice Question Options
Leonidas Zotos, Hedderik van Rijn, Malvina Nissim
In recent years, corpus-driven AI methods, such as Large Language Models (LLMs), have seen widespread use in education. While on the surface their abilities look promising for task…
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…
Puzzled By ChatGPT? No more! A Jigsaw Puzzle to Promote AI Literacy and Awareness
Francesca Padovani, Malvina Nissim
The rapid adoption of Generative AI, including LLM-based chatbots like ChatGPT, has highlighted the need for accessible ways to support public understanding and AI literacy. To add…