Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

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 …

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 …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

While there has been substantial research using adversarial attacks to analyze NLP models,
each attack is implemented in its own code repository. It remains challenging to develop …

As NLP models achieved state-of-the-art performances over benchmarks and gained wide
applications, it has been increasingly important to ensure the safe deployment of these …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Despite pre-trained language models have proven useful for learning high-quality semantic
representations, these models are still vulnerable to simple perturbations. Recent works …

Despite inextricable ties between race and language, little work has considered race in NLP
research and development. In this work, we survey 79 papers from the ACL anthology that …

Abstract While Large Language Models (LLMs) display versatile functionality, they continue
to generate harmful, biased, and toxic content, as demonstrated by the prevalence of …