31 citations · 180 across the 32 of their papers we have counts for
5 papers · 1 filter
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
Kelvin Luu, Daniel Khashabi, Suchin Gururangan +2
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In…
Expected Validation Performance and Estimation of a Random Variable's Maximum
Jesse Dodge, Suchin Gururangan, Dallas Card +2
Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we…
DEMix Layers: Disentangling Domains for Modular Language Modeling
Suchin Gururangan, Mike Lewis, Ari Holtzman +2
We introduce a new domain expert mixture (DEMix) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMix layer is a collection of expert feed…
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Elizabeth Clark, Tal August, Sofia Serrano +3
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-gener…
Detoxifying Language Models Risks Marginalizing Minority Voices
Albert Xu, Eshaan Pathak, Eric Wallace +3
Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020;…