As large language models improve, there is increasing interest in techni...
In-context learning (ICL) improves language models' performance on a var...
Recent advances in open-domain text generation models powered by large
p...
Pretrained model-based evaluation metrics have demonstrated strong
perfo...
We introduce a framework to measure how biases change before and after
f...
Recent work has shown that fine-tuning large pre-trained language models...
Current large language models can perform reasonably well on complex tas...
In NLP, models are usually evaluated by reporting single-number performa...
Large language models, which are often trained for hundreds of thousands...
Large-scale autoregressive language models such as GPT-3 are few-shot
le...
Recently, NLP models have achieved remarkable progress across a variety ...
Software traceability plays a critical role in software maintenance and
...
Multi-label image classification is the task of predicting a set of labe...
In this paper we propose VisualNews-Captioner, an entity-aware model for...
NLP models are shown to suffer from robustness issues, i.e., a model's
p...
Word embeddings derived from human-generated corpora inherit strong gend...
In this paper, we quantify, analyze and mitigate gender bias exhibited i...
In this work we analyze visual recognition tasks such as object and acti...
We introduce a new benchmark, WinoBias, for coreference resolution focus...
In this paper, we propose an inference procedure for deep convolutional
...
Language is increasingly being used to define rich visual recognition
pr...