Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …

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 …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Transformer large language models (LLMs) have sparked admiration for their exceptional
performance on tasks that demand intricate multi-step reasoning. Yet, these models …

Hallucination has been widely recognized to be a significant drawback for large language
models (LLMs). There have been many works that attempt to reduce the extent of …

Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically
improve the performance of Large Language Models (LLMs), particularly when dealing with …

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 …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

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