Teven Le Scao | DeepAI

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∙ 05/25/2023

Scaling Data-Constrained Language Models

The current trend of scaling language models involves increasing both pa...

0 Niklas Muennighoff, et al. ∙

∙ 03/07/2023

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

As language models grow ever larger, the need for large-scale high-quali...

0 Hugo Laurençon, et al. ∙

∙ 02/28/2023

Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation

A key feature of neural models is that they can produce semantic vector ...

0 Teven Le Scao, et al. ∙

∙ 11/03/2022

Crosslingual Generalization through Multitask Finetuning

Multitask prompted finetuning (MTF) has been shown to help large languag...

0 Niklas Muennighoff, et al. ∙

∙ 10/27/2022

What Language Model to Train if You Have One Million GPU Hours?

The crystallization of modeling methods around the Transformer architect...

4 Teven Le Scao, et al. ∙

∙ 04/12/2022

What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

Large pretrained Transformer language models have been shown to exhibit ...

11 Thomas Wang, et al. ∙

∙ 10/15/2021

Multitask Prompted Training Enables Zero-Shot Task Generalization

Large language models have recently been shown to attain reasonable zero...

10 Victor Sanh, et al. ∙

∙ 09/07/2021

Datasets: A Community Library for Natural Language Processing

The scale, variety, and quantity of publicly-available NLP datasets has ...

6 Quentin Lhoest, et al. ∙

∙ 03/15/2021

How Many Data Points is a Prompt Worth?

When fine-tuning pretrained models for classification, researchers eithe...

0 Teven Le Scao, et al. ∙

∙ 05/02/2020

Neural Differential Equations for Single Image Super-resolution

Although Neural Differential Equations have shown promise on toy problem...

0 Teven Le Scao, et al. ∙

∙ 09/27/2019

In-training Matrix Factorization for Parameter-frugal Neural Machine Translation

In this paper, we propose the use of in-training matrix factorization to...

0 Zachary Kaden, et al. ∙