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20232025
most citedAssessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

cs.AI2025

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…

cs.CL20241 cited

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…

cs.NE2024

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…

cs.CL2024

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…

cs.NE2023

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…