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Found 12 papers, 6 papers with code
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).
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Three Bricks to Consolidate Watermarks for Large Language Models
The task of discerning between generated and natural texts is increasingly challenging.
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
without fine-tuning the LM.
Which Discriminator for Cooperative Text Generation?
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement Learning
Secondly, they can serve as additional trajectories in the RL strategy, resulting in a teacher forcing loss weighted by the similarity of the GT to the image.
Generating artificial texts as substitution or complement of training data
The quality of artificially generated texts has considerably improved with the advent of transformers.
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 ?
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.
Choisir le bon co-équipier pour la génération coopérative de texte (Choosing The Right Teammate For Cooperative Text Generation)
Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).
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 )
Dans cet article, nous explorons comment contrôler la génération de texte au moment du décodage pour satisfaire certaines contraintes (e. g. être non toxique, transmettre certaines émotions...), sans nécessiter de ré-entrainer le modèle de langue.
"Honey, Tell Me What's Wrong", Global Explanation of Textual Discriminative Models through Cooperative Generation
Because it does not rely on initial samples, it allows to generate explanations even when data is absent (e. g., for confidentiality reasons).
