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 …

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 …

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 …

The problem of designing NLP solvers for math word problems (MWP) has seen sustained
research activity and steady gains in the test accuracy. Since existing solvers achieve high …

We introduce Dynabench, an open-source platform for dynamic dataset creation and model
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …

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

A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

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 …