Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model
with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as …

We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what
extent a given language model, such as GPT models, can simulate different aspects of …

Recent large language models (LLMs) have advanced the quality of open-ended
conversations with chatbots. Although LLM-driven chatbots have the potential to support …

Large language models (LLMs) may not equitably represent diverse global perspectives on
societal issues. In this paper, we develop a quantitative framework to evaluate whose …

With the development of large language models (LLMs), striking a balance between the
performance and safety of AI systems has never been more critical. However, the inherent …