Papers by Fanny Jourdan
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP (2023.findings-acl)
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| Challenge: | Recent debates have shown that attention maps and attribution methods are unreliable. |
| Approach: | They propose a model-agnostic XAI technique that generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task by using Non-Negative Matrix Factorization to discover concepts the model leverages to make predictions. |
| Outcome: | The proposed technique generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task without compromising the accuracy of the underlying model or requiring a new one to be trained. |
Interpreto: An Explainability Library for Transformers (2026.acl-demo)
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Antonin Poché, Thomas Mullor, Gabriele Sarti, Frédéric Boisnard, Corentin Friedrich, Charlotte Claye, Francois Hoofd, Raphael Bernas, Nicholas Asher, Celine Hudelot, Fanny Jourdan
| Challenge: | Interpreto is an open-source Python library for interpreting HuggingFace language models . it provides attribution methods and concept-based explanations . documentation or metrics are sometimes missing due to the complexity of the pipeline . |
| Approach: | Interpreto is an open-source Python library for interpreting HuggingFace language models . it provides attribution methods and concept-based explanations . authors welcome issues and pull requests . |
| Outcome: | Interpreto is an open-source Python library for interpreting HuggingFace language models . it provides attribution methods and concept-based explanations . the library welcomes issues and pull requests . |
ConSim: Measuring Concept-Based Explanations’ Effectiveness with Automated Simulatability (2025.acl-long)
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| Challenge: | Existing evaluation metrics focus only on the quality of the induced space of possible concepts, neglecting the latter. |
| Approach: | They propose to use large language models as simulators to approximate the evaluation and report various analyses to make such approximations reliable. |
| Outcome: | The proposed framework allows for scalable and consistent evaluation across models and datasets. |