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cs.LG2025
Mol-MoE: Training Preference-Guided Routers for Molecule Generation
Diego Calanzone, Pierluca D'Oro, Pierre-Luc Bacon
Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning…
cs.LG2024
Towards Logically Consistent Language Models via Probabilistic Reasoning
Diego Calanzone, Stefano Teso, Antonio Vergari
Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate…