Papers by Maxime Darrin
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews (2024.acl-long)
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| Challenge: | Scientific peer review is essential for the quality of academic publications. |
| Approach: | They propose a method that summarises scholarly reviews using a Rational Speech Act framework and novel uniqueness scores. |
| Outcome: | The proposed method generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in term of automatic metrics. |
RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data (2023.emnlp-main)
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| Challenge: | Out-of-distribution (OOD) detection is a widely covered topic in classification tasks, but most methods rely on hidden features output by the encoder. |
| Approach: | They propose to leverage soft-probabilities in a black-box framework to detect OOD . they propose to use a more operational evaluation setting to enable OOD detection . |
| Outcome: | The proposed framework can access soft-predictions but not the internal states of the model. |
COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation (2024.acl-long)
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| Challenge: | Existing methods for summarizing text are not well aligned with human judgments. |
| Approach: | They propose a task-oriented evaluation approach that assesses the quality of summarizers based on their capacity to produce summaries while preserving task outcomes. |
| Outcome: | The proposed method is able to predict task performance in a variety of contexts and tasks. |
Statistical Deficiency for Task Inclusion Estimation (2025.acl-long)
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Loïc Fosse, Frederic Bechet, Benoit Favre, Géraldine Damnati, Gwénolé Lecorvé, Maxime Darrin, Philippe Formont, Pablo Piantanida
| Challenge: | Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. |
| Approach: | They propose a theoretically grounded setup to define the notion of task and compute the inclusion between two tasks from a statistical deficiency point of view. |
| Outcome: | The proposed model estimates the degree of inclusion between tasks on synthetic data and reconstructs the classic NLP pipeline. |