4 citations · 10 across the 19 of their papers we have counts for
4 papers · 1 filter
Compartmentalised Agentic Reasoning for Clinical NLI
Maël Jullien, Lei Xu, Marco Valentino +1
Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rathe…
The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference
Maël Jullien, Marco Valentino, André Freitas
Large language models are often assumed to acquire increasingly structured, generalizable internal representations simply by scaling data and parameters. We interrogate this assump…
Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering
Marco Valentino, Geonhee Kim, Dhairya Dalal +2
Large language models (LLMs) exhibit reasoning biases, often conflating content plausibility with formal logical validity. This can lead to wrong inferences in critical domains, wh…
Integrating Expert Knowledge into Logical Programs via LLMs
Franciszek Górski, Oskar Wysocki, Marco Valentino +1
This paper introduces ExKLoP, a novel framework designed to evaluate how effectively Large Language Models (LLMs) integrate expert knowledge into logical reasoning systems. This ca…