activity
20162023
most citedReducing Transformer Depth on Demand with Structured Dropout

273 citations · 771 across the 14 of their papers we have counts for

collaborators

34 papers

cs.CL20232 cited

PaSS: Parallel Speculative Sampling

Giovanni Monea, Armand Joulin, Edouard Grave

Scaling the size of language models to tens of billions of parameters has led to impressive performance on a wide range of tasks. At generation, these models are used auto-regressi…

cs.CL20228 cited

EditEval: An Instruction-Based Benchmark for Text Improvements

Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang +6

Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text. Writing, however, is naturally an iterativ…

cs.CV20216 cited

ResMLP: Feedforward networks for image classification with data-efficient training

Hugo Touvron, Piotr Bojanowski, Mathilde Caron +8

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which…

cs.CL2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

Sewon Min, Jordan Boyd-Graber, Chris Alberti +50

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…

cs.CL202029 cited

A Memory Efficient Baseline for Open Domain Question Answering

Gautier Izacard, Fabio Petroni, Lucas Hosseini +3

Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks. While very effective, this appro…

cs.CL2020

Beyond English-Centric Multilingual Machine Translation

Angela Fan, Shruti Bhosale, Holger Schwenk +14

Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. Ho…