Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

This paper argues that a range of current AI systems have learned how to deceive humans.
We define deception as the systematic inducement of false beliefs in the pursuit of some …

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable
capabilities across image, audio, video, and text understanding. The Gemini family consists …

In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety
alignment in large language models (LLMs). This dataset uniquely separates annotations of …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

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 …

Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

In coming decades, artificial general intelligence (AGI) may surpass human capabilities at
many critical tasks. We argue that, without substantial effort to prevent it, AGIs could learn to …

Advanced AI models hold the promise of tremendous benefits for humanity, but society
needs to proactively manage the accompanying risks. In this paper, we focus on what we …