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Found 6 papers, 3 papers with code
La génération de textes artificiels en substitution ou en complément de données d’apprentissage (Generating artificial texts as substitution or complement of training data )
(ii) peuvent-elles remplacer les données d’origines quand ces dernières ne peuvent pas être distribuées, par exemple pour des raisons de confidentialité ?
Which Discriminator for Cooperative Text Generation?
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
Generative Cooperative Networks for Natural Language Generation
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
Generating artificial texts as substitution or complement of training data
The quality of artificially generated texts has considerably improved with the advent of transformers.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
without fine-tuning the LM.
