Publications | Traitement du langage naturel
Prépublications
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Are self-explanations from Large Language Models faithful?
Andreas Madsen, Sarath Chandar et Siva Reddy
In ArXiv, 2024.
#NLP
[arXiv], [code] -
Faithfulness Measurable Masked Language Models
Andreas Madsen, Siva Reddy et Sarath Chandar
In ArXiv, 2023.
#NLP
[arXiv], [code], [YouTube] -
Should We Attend More or Less? Modulating Attention for Fairness
Abdelrahman Zayed, Gonçalo Mordido, Samira Shabanian et Sarath Chandar
In ArXiv, 2023.
#NLP
[arXiv]
Articles de conférence et de revue
2024
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Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed, Gonçalo Mordido, Samira Shabanian, Ioana Baldini et Sarath Chandar
AAAI Conference on Artificial Intelligence (AAAI), 2024.
#NLP
[arXiv]
2023
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Self-Influence Guided Data Reweighting for Language Model Pre-training
Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy, Shikhar Vashishth, Sarath Chandar et Partha Talukdar
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
#NLP
[arXiv] -
EpiK-Eval: Evaluation for Language Models as Epistemic Models
Gabriele Prato, Jerry Huang, Prasanna Parthasarathi, Shagun Sodhani et Sarath Chandar
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
#NLP
[arXiv], [code] -
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
Amirhossein Kazemnejad, Mehdi Rezagholizadeh, Prasanna Parthasarathi et Sarath Chandar
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
#NLP
[arXiv] -
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Gonçalo Mordido, Hamid Palangi, Samira Shabanian et Sarath Chandar
AAAI Conference on Artificial Intelligence (AAAI), 2023.
#NLP
[arXiv]
2022
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Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Andreas Madsen, Nicholas Meade, Vaibhav Adlakha et Siva Reddy
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
[BlackboxNLP Workshop, 2022]
#NLP
[arXiv], [code] -
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq et Sarath Chandar
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
#NLP
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Local Structure Matters Most in Most Languages
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq et Sarath Chandar
Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP), 2022.
#NLP
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Post-hoc Interpretability for Neural NLP: A Survey
Andreas Madsen, Siva Reddy et Sarath Chandar
ACM Computing Surveys, 2022.
#NLP
[arXiv] -
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq et Sarath Chandar
Findings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
#NLP
[arXiv]
2021
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MLMLM: Link Prediction with Mean Likelihood Masked Language Model
Louis Clouâtre, Philippe Trempe, Amal Zouaq et Sarath Chandar
Findings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
#NLP
[arXiv] -
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
Charan Reddy, Deepak Sharma, Soroush Mehri, Adriana Romero, Samira Shabanian et Sina Honari
Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021.
#NLP
[openreview], [code] -
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi, Mohamed Abdelsalam, Joelle Pineau et Sarath Chandar
Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue, 2021.
#NLP
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Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Prasanna Parthasarathi, Sarath Chandar et Joelle Pineau
Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue, 2021.
#NLP
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A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura et Eduard Hovy
Findings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
#NLP
[arXiv]