51 citations · 266 across the 32 of their papers we have counts for
5 papers · 1 filter
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
Max Bartolo, Tristan Thrush, Sebastian Riedel +3
In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected trainin…
FLAVA: A Foundational Language And Vision Alignment Model
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami +4
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally,…
Analyzing Dynamic Adversarial Training Data in the Limit
Eric Wallace, Adina Williams, Robin Jia +1
To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena. Dynamic adversarial data collec…
What's Hidden in a One-layer Randomly Weighted Transformer?
Sheng Shen, Zhewei Yao, Douwe Kiela +2
We demonstrate that, hidden within one-layer randomly weighted neural networks, there exist subnetworks that can achieve impressive performance, without ever modifying the weight i…
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush +6
We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform e…