Machine-learned language models have transformed everyday life: they steer us
when we study, drive, manage money. They have the potential to transform our
civilization. But they hallucinate. Their realities are virtual. This note
provides a high-level overview of language models and outlines a low-level
model of learning machines. It turns out that, after they become capable of
recognizing hallucinations and dreaming safely, as humans tend to be, the
language-learning machines proceed to generate broader systems of false beliefs
and self-confirming theories, as humans tend to do.

机器学习的语言模型已经改变了我们的生活，但它们也会产生幻觉和错误的信念，本文提供了对语言模型的概述，并介绍了学习机器的低级模型。

人类和计算机中的语言处理

Language processing in humans and computers

Large language models (LLMs) are currently at the forefront of intertwining
artificial intelligence (AI) systems with human communication and everyday
life. Thus, aligning them with human values is of great importance. However,
given the steady increase in reasoning abilities, future LLMs are under
suspicion of becoming able to deceive human operators and utilizing this
ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to
possess a conceptual understanding of deception strategies. This study reveals
that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were
non-existent in earlier LLMs. We conduct a series of experiments showing that
state-of-the-art LLMs are able to understand and induce false beliefs in other
agents, that their performance in complex deception scenarios can be amplified
utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in
LLMs can alter their propensity to deceive. In sum, revealing hitherto unknown
machine behavior in LLMs, our study contributes to the nascent field of machine
psychology.

该研究揭示了现有的大型语言模型存在伪装策略的能力，分析了它们在复杂欺骗场景中的表现，并发现引发机器心理学领域的之前未知的机器行为。