Diego Calanzone

Publications

1. Reasoning

   Towards Logically Consistent Language Models via Probabilistic Reasoning

   Diego Calanzone, Stefano Teso, and Antonio Vergari

   ICLR 2024 Workshop on Reliable and Responsible Foundation Models

   Abs

   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 non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically.

2. AI Policy

   An open source perspective on AI and alignment with the EU AI Act

   Diego Calanzone, Andrea Coppari, Riccardo Tedoldi, and 2 more authors

   AISafety/SafeRL@ IJCAI 2023

   Abs

   Artificial intelligence systems based on deep learning have increasingly received interest due to their success in complex human tasks. A current trend in deep learning is to study how algorithms learn multiple new abilities as their size and training data increase." General purpose AI"(GPAI), that is systems that can transfer the acquired knowledge to solve multiple tasks, are candidate to constitute the backbone of many AI algorithms applied in specific fields on industry, eg healthcare, customer support, administration. While various research laboratories express safety concerns on GPAI and do not openly share access to their algorithms, others advocate for their" democratization" and an increasing amount of open-source versions is available online. In this study we analyze this phenomenon from two perspectives and try to reconcile them. From one side, research communities support open collaborations, free access to knowledge and resources; on the other, political institutions, involved in the orchestration between the support for innovation and the control of societal impact, aim at preventing violations of fundamental human rights. We particularly focus on the European approach for risk assessment of AI systems. In our opinion, it greatly overlaps with work in ethics and law conducted by AI researchers (eg the Stanford Centre for Research on Foundation Models). Specifically we identify some necessary modifications to improve coordination between the two sides, while also discussing viable implementations in the technical field.