  Large Language Model (LLMs) such as ChatGPT that exhibit generative AI
capabilities are facing accelerated adoption and innovation. The increased
presence of Generative AI (GAI) inevitably raises concerns about the risks and
safety associated with these models. This article provides an up-to-date survey
of recent trends in AI safety research of GAI-LLMs from a computer scientist's
perspective: specific and technical. In this survey, we explore the background
and motivation for the identified harms and risks in the context of LLMs being
generative language models; our survey differentiates by emphasising the need
for unified theories of the distinct safety challenges in the research
development and applications of LLMs. We start our discussion with a concise
introduction to the workings of LLMs, supported by relevant literature. Then we
discuss earlier research that has pointed out the fundamental constraints of
generative models, or lack of understanding thereof (e.g., performance and
safety trade-offs as LLMs scale in number of parameters). We provide a
sufficient coverage of LLM alignment -- delving into various approaches,
contending methods and present challenges associated with aligning LLMs with
human preferences. By highlighting the gaps in the literature and possible
implementation oversights, our aim is to create a comprehensive analysis that
provides insights for addressing AI safety in LLMs and encourages the
development of aligned and secure models. We conclude our survey by discussing
future directions of LLMs for AI safety, offering insights into ongoing
research in this critical area.
