563 citations · 573 across the 6 of their papers we have counts for
6 papers · 1 filter
"Honey, Tell Me What's Wrong", Global Explanation of Textual Discriminative Models through Cooperative Generation
Antoine Chaffin, Julien Delaunay
The ubiquity of complex machine learning has raised the importance of model-agnostic explanation algorithms. These methods create artificial instances by slightly perturbing real i…
Three Bricks to Consolidate Watermarks for Large Language Models
Pierre Fernandez, Antoine Chaffin, Karim Tit +2
The task of discerning between generated and natural texts is increasingly challenging. In this context, watermarking emerges as a promising technique for ascribing generated text…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Which Discriminator for Cooperative Text Generation?
Antoine Chaffin, Thomas Scialom, Sylvain Lamprier +4
Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external informat…
Generating artificial texts as substitution or complement of training data
Vincent Claveau, Antoine Chaffin, Ewa Kijak
The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised l…
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Antoine Chaffin, Vincent Claveau, Ewa Kijak
Large language models (LM) based on Transformers allow to generate plausible long texts. In this paper, we explore how this generation can be further controlled at decoding time to…