Large language models (LLMs) have led to a surge in collaborative writing with model
assistance. As different users incorporate suggestions from the same model, there is a risk of …

As major progress is made in open-ended text generation, measuring how close machine-
generated text is to human language remains a critical open problem. We introduce Mauve …

Knowledge distillation (KD) is widely used for compressing a teacher model to a smaller
student model, reducing its inference cost and memory footprint while preserving model …

Generative artificial intelligence has made significant strides, producing text
indistinguishable from human prose and remarkably photorealistic images. Automatically …

Contrastive representation learning seeks to acquire useful representations by estimating
the shared information between multiple views of data. Here, the choice of data …

It is often desirable to distill the capabilities of large language models (LLMs) into smaller
student models due to compute and memory constraints. One way to do this for classification …

A good automatic evaluation metric for language generation ideally correlates highly with
human judgements of text quality. Yet, there is a dearth of such metrics, which inhibits the …

Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …

As we expect to see a rapid integration of generative models in our day to day lives, the
development of rigorous methods of evaluation and analysis for generative models has …

The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on
their own private data. However, this practice raises privacy concerns due to the …