194 citations · 692 across the 15 of their papers we have counts for
47 papers
Human-Adversarial Visual Question Answering
Sasha Sheng, Amanpreet Singh, Vedanuj Goswami +4
Performance on the most commonly used Visual Question Answering dataset (VQA v2) is starting to approach human accuracy. However, in interacting with state-of-the-art VQA models, i…
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton +1
In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that…
True Few-Shot Learning with Language Models
Ethan Perez, Douwe Kiela, Kyunghyun Cho
Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning,…
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
Shir Gur, Natalia Neverova, Chris Stauffer +3
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…
Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou +1
We propose the first general-purpose gradient-based attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of advers…
Retrieval Augmentation Reduces Hallucination in Conversation
Kurt Shuster, Spencer Poff, Moya Chen +2
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et…