activity
20182022
most citedA Memory Efficient Baseline for Open Domain Question Answering

29 citations · 43 across the 3 of their papers we have counts for

collaborators

8 papers

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

Autoregressive Entity Retrieval

Nicola De Cao, Gautier Izacard, Sebastian Riedel +1

Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article).…

cs.CL2020

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Gautier Izacard, Edouard Grave

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models w…