Neural networks for NLP are becoming increasingly complex and widespread, and there is a
growing concern if these models are responsible to use. Explaining models helps to address …

Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

The impressive performance of recent language models across a wide range of tasks
suggests that they possess a degree of abstract reasoning skills. Are these skills general …

Pretraining data design is critically under-documented and often guided by empirically
unsupported intuitions. We pretrain models on data curated (1) at different collection …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Large-scale diffusion models have achieved state-of-the-art results on text-to-image
synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we …

Large language models (LLMs) show impressive abilities via few-shot prompting.
Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world …

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural
Language Generation (NLG), ie, evaluating the generated text from multiple explainable …

Without proper safeguards, large language models will readily follow malicious instructions
and generate toxic content. This risk motivates safety efforts such as red-teaming and large …