For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

Machine-generated text is increasingly difficult to distinguish from text authored by humans.
Powerful open-source models are freely available, and user-friendly tools that democratize …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Language models are increasingly attracting interest from writers. However, such models
lack long-range semantic coherence, limiting their usefulness for longform creative writing …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

It is well known that the standard likelihood training and approximate decoding objectives in
neural text generation models lead to less human-like responses for open-ended tasks such …

We consider the problem of automatically generating longer stories of over two thousand
words. Compared to prior work on shorter stories, long-range plot coherence and relevance …

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 …