9 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…
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…