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 …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Without proper safeguards, large language models will readily follow malicious instructions
and generate toxic content. This risk motivates safety efforts such as red-teaming and large …

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 …

Current literature, aiming to surpass the" Chain-of-Thought" approach, often resorts to
external modi operandi involving halting, modifying, and then resuming the generation …

Logical reasoning is fundamental for humans yet presents a substantial challenge in the
domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and …

While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

Vision-language models (VLMs) have recently demonstrated strong efficacy as visual
assistants that can parse natural queries about the visual content and generate human-like …

Companies, organizations, and governments increasingly exploit Language Models'(LM)
remarkable capability to display agent-like behavior. As LMs are adopted to perform tasks …