1 citations · 1 across the 7 of their papers we have counts for
8 papers
LumiXAI: A Modular Full-Stack Framework for Feature Attribution
Alfio Ferrara, Lorenzo Gatta, Sergio Picascia +1
Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools specialize along narrow axes, suc…
Refusal Beyond a Single Direction: A Preliminary Comparison of Diff-in-Means and INLP
Elisabetta Rocchetti, Alfio Ferrara
Arditi et al. (2024) has shown that refusal in safety fine-tuned chat models is mediated by a single linear direction in the residual stream, recoverable by a difference-in-means (…
How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism
Elisabetta Rocchetti, Alfio Ferrara
Instruction tuning is commonly assumed to endow language models with a domain-general ability to follow instructions, yet the underlying mechanism remains poorly understood. Does i…
Modeling Transformers as complex networks to analyze learning dynamics
Elisabetta Rocchetti
The process by which Large Language Models (LLMs) acquire complex capabilities during training remains a key open question in mechanistic interpretability. This project investigate…
How Instruction-Tuning Imparts Length Control: A Cross-Lingual Mechanistic Analysis
Elisabetta Rocchetti, Alfio Ferrara
Adhering to explicit length constraints, such as generating text with a precise word count, remains a significant challenge for Large Language Models (LLMs). This study aims at inv…
What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content
Alfio Ferrara, Sergio Picascia, Laura Pinnavaia +3
Proprietary Large Language Models (LLMs) have shown tendencies toward politeness, formality, and implicit content moderation. While previous research has primarily focused on expli…
The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models
Alfio Ferrara, Sergio Picascia, Elisabetta Rocchetti
Text-to-image diffusion models have demonstrated remarkable capabilities in generating artistic content by learning from billions of images, including popular artworks. However, th…
Unveiling Transformer Perception by Exploring Input Manifolds
Alessandro Benfenati, Alfio Ferrara, Alessio Marta +2
This paper introduces a general method for the exploration of equivalence classes in the input space of Transformer models. The proposed approach is based on sound mathematical the…