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 …

State-of-the-art natural language processing (NLP) models are trained on massive training
corpora, and report a superlative performance on evaluation datasets. This survey delves …

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 …

Hundreds of millions of people now interact with language models, with uses ranging from
help with writing, to informing hiring decisions. However, these language models are known …

Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning,
using fewer trainable parameters. Several strategies (eg, Adapters, prefix tuning, BitFit, and …

Dialect differences caused by regional, social, and economic factors cause performance
discrepancies for many groups of language technology users. Inclusive and equitable …

We evaluate how well LLMs understand African American Language (AAL) in comparison to
their performance on White Mainstream English (WME), the encouraged" standard" form of …

Rapid advancements of large language models (LLMs) have enabled the processing,
understanding, and generation of human-like text, with increasing integration into systems …

Current large language models demonstrate deficiencies in understanding low-resource
languages, particularly the minority languages in China. This limitation stems from the …

Hundreds of millions of people now interact with language models, with uses ranging from
serving as a writing aid to informing hiring decisions. Yet these language models are known …