Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Even the largest neural networks make errors, and once-correct predictions can become
invalid as the world changes. Model editors make local updates to the behavior of base (pre …

Language Models (LMs) often cannot be deployed because of their potential to harm users
in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using …

Building open-domain chatbots is a challenging area for machine learning research. While
prior work has shown that scaling neural models in the number of parameters and the size of …

Large-scale language models often learn behaviors that are misaligned with user
expectations. Generated text may contain offensive or toxic language, contain significant …

Neural abstractive summarization models are prone to generate content inconsistent with
the source document, ie unfaithful. Existing automatic metrics do not capture such mistakes …

A large array of pretrained models are available to the biomedical NLP (BioNLP) community.
Finding the best model for a particular task can be difficult and time-consuming. For many …

Current language models can generate high-quality text. Are they simply copying text they
have seen before, or have they learned generalizable linguistic abstractions? To tease apart …

Today's probabilistic language generators fall short when it comes to producing coherent
and fluent text despite the fact that the underlying models perform well under standard …