4 citations · 8 across the 7 of their papers we have counts for
7 papers
Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations
Antonin Poché, Fanny Jourdan, Nils Feldhus +6
Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is co…
Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics
Raphael Bernas, Fanny Jourdan, Antonin Poché +1
Since their introduction, Transformer architectures have dominated Natural Language Processing (NLP). However, recent research has highlighted an inherent anisotropy phenomenon in…
Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
Guilhem Fouilhé, Rebecca Eifler, Antonin Poché +2
When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elici…
Interpreto: An Explainability Library for Transformers
Antonin Poché, Thomas Mullor, Gabriele Sarti +8
Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attri…
ConSim: Measuring Concept-Based Explanations' Effectiveness with Automated Simulatability
Antonin Poché, Alon Jacovi, Agustin Martin Picard +2
Concept-based explanations work by mapping complex model computations to human-understandable concepts. Evaluating such explanations is very difficult, as it includes not only the…
Natural Example-Based Explainability: a Survey
Antonin Poché, Lucas Hervier, Mohamed-Chafik Bakkay
Explainable Artificial Intelligence (XAI) has become increasingly significant for improving the interpretability and trustworthiness of machine learning models. While saliency maps…
Xplique: A Deep Learning Explainability Toolbox
Thomas Fel, Lucas Hervier, David Vigouroux +12
Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, b…