Neural Interpretable Reasoning

We formalize a novel modeling framework for achieving interpretability in deep learning, anchored in the principle of inference equivariance. While the direct verification of interpretability scales exponentially with the number of variables of the system, we show that this complexity can be mitigated by treating interpretability as a Markovian property and employing neural re-parametrization techniques. Building on these insights, we propose a new modeling paradigm -- neural generation and interpretable execution -- that enables scalable verification of equivariance. This paradigm provides a general approach for designing Neural Interpretable Reasoners that are not only expressive but also transparent.

Self-supervised Interpretable Concept-based Models for Text Classification

Despite their success, Large-Language Models (LLMs) still face criticism as their lack of interpretability limits their controllability and reliability. Traditional post-hoc interpretation methods, based on attention and gradient-based analysis, offer limited insight into the model's decision-making processes. In the image field, Concept-based models have emerged as explainable-by-design architectures, employing human-interpretable features as intermediate representations. However, these methods have not been yet adapted to textual data, mainly because they require expensive concept annotations, which are impractical for real-world text data. This paper addresses this challenge by proposing a self-supervised Interpretable Concept Embedding Models (ICEMs). We leverage the generalization abilities of LLMs to predict the concepts labels in a self-supervised way, while we deliver the final predictions with an interpretable function. The results of our experiments show that ICEMs can be trained in a self-supervised way achieving similar performance to fully supervised concept-based models and end-to-end black-box ones. Additionally, we show that our models are (i) interpretable, offering meaningful logical explanations for their predictions; (ii) interactable, allowing humans to modify intermediate predictions through concept interventions; and (iii) controllable, guiding the LLMs' decoding process to follow a required decision-making path.