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Maxime Darrin

maxime.darrin@mila.quebec
PhD - McGill University
Supervisor
Jackie Cheung
Co-supervisor
Pablo Piantanida
Website
Github
Google Scholar

Publications

COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin
Philippe Formont
Jackie Chi Kit Cheung
Pablo Piantanida
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (see more)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce
2024-02-29
ArXiv (preprint)
doi.org
arxiv.org
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
Maxime Darrin
Pablo Piantanida
Pierre Colombo
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots… (see more), is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.
2023-12-01
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
arxiv.org
RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data
Maxime Darrin
Pablo Piantanida
Pierre Colombo
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots… (see more), is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.
2023-10-07
EMNLP/2023/Conference (accepted)
openreview.net
openreview.net
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Maxime Darrin
Guillaume Staerman
Eduardo Dadalto Câmara Gomes
Jackie Ck Cheung
Pablo Piantanida
Pierre Colombo
2023-02-20
ArXiv (preprint)
doi.org
arxiv.org