9 papers · 1 filter
Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation
Nura Aljaafari, Marco Valentino, André Freitas
Predicting a label correctly does not necessarily require representing the operation that produces it. Transformer representations are known to carry label-level information, but w…
GeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving
Yingji Zhang, Yong Dai, André Freitas
Mathematical reasoning is a hallmark of human intelligence, requiring logical deduction, symbolic manipulation, and abstract thinking. Recent multimodal large language models (MLLM…
Inferring Latent Intentions: Attributional Natural Language Inference in LLM Agents
Xin Quan, Jiafeng Xiong, Marco Valentino +1
Attributional inference, the ability to predict latent intentions behind observed actions, is a critical yet underexplored capability for large language models (LLMs) operating in…
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino +1
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature,…
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…
Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference
Geonhee Kim, Marco Valentino, André Freitas
Recent studies on reasoning in language models (LMs) have sparked a debate on whether they can learn systematic inferential principles or merely exploit superficial patterns in the…