32 citations · 62 across the 21 of their papers we have counts for
21 papers
Automated Interpretability and Feature Discovery in Language Models with Agents
Arnau Marin-Llobet, Javier Ferrando
We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large language models. The system r…
Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation
Nurkhan Laiyk, Gerard I. Gállego, Javier Ferrando +1
Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that multilingual model represent…
Language Models Can Explain Visual Features via Steering
Javier Ferrando, Enrique Lopez-Cuena, Pablo Agustin Martin-Torres +3
Sparse Autoencoders uncover thousands of features in vision models, yet explaining these features without requiring human intervention remains an open challenge. While previous wor…
Weight space Detection of Backdoors in LoRA Adapters
David Puertolas Merenciano, Ekaterina Vasyagina, Kevin Zhu +2
LoRA adapters let users fine-tune large language models (LLMs) efficiently. However, LoRA adapters are shared through open repositories like Hugging Face Hub \citep{huggingface_hub…
Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability
Sergi Masip, Gido M. van de Ven, Javier Ferrando +1
Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying mechanisms. We introduce a mechani…
Real-Time Detection of Hallucinated Entities in Long-Form Generation
Oscar Obeso, Andy Arditi, Javier Ferrando +3
Large language models are now routinely used in high-stakes applications where hallucinations can cause serious harm, such as medical consultations or legal advice. Existing halluc…