10 papers
From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
Nura Aljaafari, Danilo S. Carvalho, Andre Freitas
Mechanistic interpretability produces circuit-level causal analyses of neural network behaviour, but discovered circuits often remain isolated experimental artefacts: there is no s…
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…
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…
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…
Interpreting token compositionality in LLMs: A robustness analysis
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Understanding the internal mechanisms of large language models (LLMs) is integral to enhancing their reliability, interpretability, and inference processes. We present Constituent-…
Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis
João Pedro Gandarela, Danilo S. Carvalho, André Freitas
This work presents a novel systematic methodology to analyse the capabilities and limitations of Large Language Models (LLMs) with feedback from a formal inference engine, on logic…
Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models
Nura Aljaafari, Danilo S. Carvalho, André Freitas
Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies have attributed functional roles…