An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by tuning the model in a way that enhances desired behaviors and inhibits undesired ones, a process referred to as alignment. In this paper, we propose a theoretical approach called Behavior Expectation Bounds (BEB) which allows us to formally investigate several inherent characteristics and limitations of alignment in large language models. Importantly, we prove that within the limits of this framework, for any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt. This implies that any alignment process that attenuates an undesired behavior but does not remove it altogether, is not safe against adversarial prompting attacks. Furthermore, our framework hints at the mechanism by which leading alignment approaches such as reinforcement learning from human feedback make the LLM prone to being prompted into the undesired behaviors. This theoretical result is being experimentally demonstrated in large scale by the so called contemporary "chatGPT jailbreaks", where adversarial users trick the LLM into breaking its alignment guardrails by triggering it into acting as a malicious persona. Our results expose fundamental limitations in alignment of LLMs and bring to the forefront the need to devise reliable mechanisms for ensuring AI safety.

翻译：使语言模型的行为对人类用户有用且无害，是与人类交互的语言模型开发中的一个重要方面。这通常通过调整模型来增强期望行为、抑制非期望行为实现，这一过程被称为对齐。在本文中，我们提出了一种称为行为期望边界（BEB）的理论方法，使得能够正式研究大型语言模型对齐的若干固有特征和局限性。重要的是，我们证明在该框架的范围内，对于任何有有限概率被模型展现出的行为，都存在可以触发模型输出该行为的提示词，并且该概率随提示词长度增加而增大。这意味着，任何减弱非期望行为但未完全消除该行为的对齐过程，都无法抵御对抗性提示攻击。此外，我们的框架揭示了诸如基于人类反馈的强化学习等主流对齐方法，使大语言模型容易受到诱导而输出非期望行为的机制。这一理论结果已被大规模实验所证实，即当代所谓的“ChatGPT越狱”现象——对抗性用户通过触发模型扮演恶意角色，从而突破其对齐护栏。我们的研究揭示了大语言模型对齐的根本局限性，并凸显了设计可靠机制以确保人工智能安全的迫切需求。