194 citations · 692 across the 15 of their papers we have counts for
32 papers · 1 filter
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,…
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…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little
Koustuv Sinha, Robin Jia, Dieuwke Hupkes +3
A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent i…