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 …

Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded
global healthcare system, which receives approximately 60 million primary care visits and 6 …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Large language models (LLMs) are currently at the forefront of intertwining AI systems with
human communication and everyday life. Thus, aligning them with human values is of great …

Recent years have witnessed remarkable progress made in large language models (LLMs).
Such advancements, while garnering significant attention, have concurrently elicited various …

Large language models (LLMs) can" lie", which we define as outputting false statements
despite" knowing" the truth in a demonstrable sense. LLMs might" lie", for example, when …

Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs)
such as GPT and BERT have evolved significantly, impacting various industries with their …

In this paper, we present JADE, a targeted linguistic fuzzing platform which strengthens the
linguistic complexity of seed questions to simultaneously and consistently break a wide …

Forecasts of future events are essential inputs into informed decision-making. Machine
learning (ML) systems have the potential to deliver forecasts at scale, but there is no …

State-of-the-art Large Language Models have recently exhibited extraordinary linguistic
abilities which have surprisingly extended to reasoning. However, responses that are …