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cs.CL2025
How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects
Leonardo Bertolazzi, Sandro Pezzelle, Raffaella Bernardi
Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning problem influences judgments regarding…
cs.CL2025
Teaching Small Language Models to Learn Logic through Meta-Learning
Leonardo Bertolazzi, Manuel Vargas Guzmán, Raffaella Bernardi +2
Large language models (LLMs) are increasingly evaluated on reasoning tasks, yet their logical abilities remain contested. To address this, we study LLMs' reasoning in a well-define…
cs.CL2025
The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It
Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank +1
The ability of large language models (LLMs) to validate their output and identify potential errors is crucial for ensuring robustness and reliability. However, current research ind…