97 citations · 281 across the 14 of their papers we have counts for
14 papers
The Limits of Automatic Evaluation of Creativity in Large Language Models
Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi
Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generat…
On the Creativity of AI Agents
Giorgio Franceschelli, Mirco Musolesi
Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across multiple domains. Whether the…
Complexity-Regularized Proximal Policy Optimization
Luca Serfilippi, Giorgio Franceschelli, Antonio Corradi +1
Policy gradient methods usually rely on entropy regularization to prevent premature convergence. However, maximizing entropy indiscriminately pushes the policy towards a uniform di…
Quantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning
Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli +3
The inverse design of molecules has challenged chemists for decades. In the past years, machine learning and artificial intelligence have emerged as new tools to generate molecules…
DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation
Giorgio Franceschelli, Mirco Musolesi
Despite their growing capabilities, language models still frequently reproduce content from their training data, generate repetitive text, and favor common grammatical patterns and…
Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation
Giorgio Franceschelli, Mirco Musolesi
Despite the increasing use of large language models for creative tasks, their outputs often lack diversity. Common solutions, such as sampling at higher temperatures, can compromis…
Training Foundation Models as Data Compression: On Information, Model Weights and Copyright Law
Giorgio Franceschelli, Claudia Cevenini, Mirco Musolesi
The training process of foundation models as for other classes of deep learning systems is based on minimizing the reconstruction error over a training set. For this reason, they a…
Creative Beam Search: LLM-as-a-Judge For Improving Response Generation
Giorgio Franceschelli, Mirco Musolesi
Large language models are revolutionizing several areas, including artificial creativity. However, the process of generation in machines profoundly diverges from that observed in h…
Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning
Giorgio Franceschelli, Mirco Musolesi
The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain. Here, we ask if the same is true for reinforcement learning agents as well. Given…
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
Giorgio Franceschelli, Mirco Musolesi
Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged a…
On the Creativity of Large Language Models
Giorgio Franceschelli, Mirco Musolesi
Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling…
DeepCreativity: Measuring Creativity with Deep Learning Techniques
Giorgio Franceschelli, Mirco Musolesi
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for a…
Copyright in Generative Deep Learning
Giorgio Franceschelli, Mirco Musolesi
Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created…
Creativity and Machine Learning: A Survey
Giorgio Franceschelli, Mirco Musolesi
There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity the…