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5 papers
Learning neuro-symbolic convergent term rewriting systems
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Building neural systems that can learn to execute symbolic algorithms is a challenging open problem in artificial intelligence, especially when aiming for strong generalization and…
Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Large Language Models (LLMs) achieve impressive performance in a wide range of tasks, even if they are often trained with the only objective of chatting fluently with users. Among…
A Neural Rewriting System to Solve Algorithmic Problems
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem ins…
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide vari…
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Solving symbolic reasoning problems that require compositionality and systematicity is considered one of the key ingredients of human intelligence. However, symbolic reasoning is s…