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Generating Artificial Texts as Substitution or Complement of Training Data., , and . LREC, page 4260-4269. European Language Resources Association, (2022)Generating artificial texts as substitution or complement of training data., , and . CoRR, (2021)Generative Cooperative Networks for Natural Language Generation., , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 11891-11905. PMLR, (2022)Décodage guidé par un discriminateur avec le Monte Carlo Tree Search pour la génération de texte contrainte (Discriminator-guided decoding with Monte Carlo Tree Search for constrained text generation )., , and . TALN-RECITAL, page 27-41. ATALA, (2022)"Honey, Tell Me What's Wrong", Global Explanation of Textual Discriminative Models through Cooperative Generation., and . BlackboxNLP@EMNLP, page 76-88. Association for Computational Linguistics, (2023)La génération de textes artificiels en substitution ou en complément de données d'apprentissage (Generating artificial texts as substitution or complement of training data )., , and . TALN (1), page 37-49. ATALA, (2021)Three Bricks to Consolidate Watermarks for Large Language Models., , , , and . WIFS, page 1-6. IEEE, (2023)Generating texts under constraint through discriminator-guided MCTS., , and . CoRR, (2021)PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding., , and . NAACL-HLT, page 2953-2967. Association for Computational Linguistics, (2022)Multimodal misinformation detection overcoming the training data collection challenge through data generation. (Détection de désinformation multimodale : surmonter le défi de la collecte de données d'entraînement grâce à la génération de données).. University of Rennes 1, France, (2023)