Papers by Nathan Noiry
Toward Stronger Textual Attack Detectors (2023.findings-emnlp)
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| Challenge: | despite the high performance of deep learning techniques, trained models remain vulnerable to adversarial attacks . authors present LAROUSSE, STAKEOUT and other approaches to detect adversarials . LARousSE is unsupervised, hyperparameter free and non-differentiable . |
| Approach: | They propose a framework to detect adversarial attacks and an extended benchmark to test them . they demonstrate that LAROUSSE outperforms previous methods and allows to identify interesting factor of detection rate variations. |
| Outcome: | The proposed framework outperforms existing methods and allows to identify interesting factor of detection rate variations. |
Learning Disentangled Textual Representations via Statistical Measures of Similarity (2022.acl-long)
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| Challenge: | Existing approaches to disentangle a sensitive attribute from textual representations require training and multiple parameter updates. |
| Approach: | They propose a family of regularizers for learning disentangled representations that do not require training. |
| Outcome: | The proposed regularizers are faster and faster and achieve better results when combined with pretrained and randomly initialized text encoders. |
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks (2024.findings-emnlp)
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| Challenge: | Existing benchmarking approaches assume that all systems have scores available for all tasks, which is not always practical. |
| Approach: | They propose a method to benchmark when some systems have scores missing on a task . they use a compatible partial ranking approach to impute missing data . |
| Outcome: | The proposed method is validated on 131 million scores, larger than existing benchmarks. |