32 citations · 85 across the 50 of their papers we have counts for
11 papers · 1 filter
MASA: LLM-Driven Multi-Agent Systems for Autoformalization
Lan Zhang, Marco Valentino, André Freitas
Autoformalization serves a crucial role in connecting natural language and formal reasoning. This paper presents MASA, a novel framework for building multi-agent systems for autofo…
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…
Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies
Mael Jullien, Marco Valentino, Leonardo Ranaldi +1
Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectivene…
Beyond Gold Standards: Epistemic Ensemble of LLM Judges for Formal Mathematical Reasoning
Lan Zhang, Marco Valentino, Jordan Meadows +1
Statement autoformalization plays a crucial role in formal mathematical reasoning by enabling the automatic translation of natural language statements into formal languages. While…
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations
Xin Quan, Marco Valentino, Louise A. Dennis +1
Natural language explanations play a fundamental role in Natural Language Inference (NLI) by revealing how premises logically entail hypotheses. Recent work has shown that the inte…
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…
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement
Xin Quan, Marco Valentino, Danilo S. Carvalho +2
A persistent challenge in AI is the effective integration of material and formal inference - the former concerning the plausibility and contextual relevance of arguments, while the…
Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions
Leonardo Ranaldi, Marco Valentino, Andrè Freitas
Chain-of-Though (CoT) represents a common strategy for reasoning in Large Language Models (LLMs) by decomposing complex tasks into intermediate inference steps. However, explanatio…
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…
Autoformalization in the Wild: Assessing LLMs on Real-World Mathematical Definitions
Lan Zhang, Marco Valentino, Andre Freitas
Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge the gap between informal mathematics and formal languages through autoformalization. However, it is sti…