Papers by Antonin Poché

2 papers
Interpreto: An Explainability Library for Transformers (2026.acl-demo)

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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.

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