9 papers · 1 filter
Emergence and Localisation of Semantic Role Circuits in LLMs
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Despite displaying semantic competence, large language models' internal mechanisms that ground abstract semantic structure remain insufficiently characterised. We propose a method…
elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings
Philip Osborne, Danilo S. Carvalho, André Freitas
We present elsciRL, an open-source Python library to facilitate the application of language solutions on reinforcement learning problems. We demonstrate the potential of our softwa…
TRACE: Training and Inference-Time Interpretability Analysis for Language Models
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Understanding when and how linguistic knowledge emerges during language model training remains a central challenge for interpretability. Most existing tools are post hoc, rely on s…
Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
Yingji Zhang, Danilo S. Carvalho, André Freitas
Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, compositionality, and generalisatio…
Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study
Yingji Zhang, Marco Valentino, Danilo S. Carvalho +1
Incorporating explicit reasoning rules within the latent space of language models (LMs) offers a promising pathway to enhance generalisation, interpretability, and controllability.…
TRACE for Tracking the Emergence of Semantic Representations in Transformers
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Modern transformer models exhibit phase transitions during training, distinct shifts from memorisation to abstraction, but the mechanisms underlying these transitions remain poorly…
Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret the continuous numerical output…
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…
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. While archit…