Controllable Text Generation (CTG) is an emerging area in the field of natural language
generation (NLG). It is regarded as crucial for the development of advanced text generation …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

Language model (LM) pretraining can learn various knowledge from text corpora, helping
downstream tasks. However, existing methods such as BERT model a single document, and …

As machine learning methods are deployed in real-world settings such as healthcare, legal
systems, and social science, it is crucial to recognize how they shape social biases and …

Using topic modeling and lexicon-based word similarity, we find that stories generated by
GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and …

Answering complex questions about textual narratives requires reasoning over both stated
context and the world knowledge that underlies it. However, pretrained language models …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

Despite recent advances in natural language generation, it remains challenging to control
attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time …

Language models (LMs) have recently shown remarkable performance on reasoning tasks
by explicitly generating intermediate inferences, eg, chain-of-thought prompting. However …