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。

大型语言模型中出现的欺骗能力

Deception Abilities Emerged in Large Language Models

We consider a team of autonomous agents that navigate in an adversarial
environment and aim to achieve a task by allocating their resources over a set
of target locations. The adversaries in the environment observe the autonomous
team's behavior to infer their objective and counter-allocate their own
resources to the target locations. In this setting, we develop strategies for
controlling the density of the autonomous team so that they can deceive the
adversaries regarding their objective while achieving the desired final
resource allocation. We first develop a prediction algorithm, based on the
principle of maximum entropy, to express the team's behavior expected by the
adversaries. Then, by measuring the deceptiveness via Kullback-Leibler
divergence, we develop convex optimization-based planning algorithms that
deceives adversaries by either exaggerating the behavior towards a decoy
allocation strategy or creating ambiguity regarding the final allocation
strategy. Finally, we illustrate the performance of the proposed algorithms
through numerical simulations.

本文研究在敌对环境下，自主代理团队通过在目标位置上分配资源来完成任务，发展了密度控制和最大熵预测算法，以欺骗对手达到期望的资源分配。