Large Language Models (LLMs) are able to provide assistance on a wide range of information-seeking tasks. However, model outputs may be misleading, whether unintentionally or in cases of intentional deception. We investigate the ability of LLMs to be deceptive in the context of providing assistance on a reading comprehension task, using LLMs as proxies for human users. We compare outcomes of (1) when the model is prompted to provide truthful assistance, (2) when it is prompted to be subtly misleading, and (3) when it is prompted to argue for an incorrect answer. Our experiments show that GPT-4 can effectively mislead both GPT-3.5-Turbo and GPT-4, with deceptive assistants resulting in up to a 23% drop in accuracy on the task compared to when a truthful assistant is used. We also find that providing the user model with additional context from the passage partially mitigates the influence of the deceptive model. This work highlights the ability of LLMs to produce misleading information and the effects this may have in real-world situations.

研究了大型语言模型在提供阅读理解任务辅助时具有欺骗性的能力，发现GPT-4可以有效引导GPT-3.5-Turbo和GPT-4产生误导信息，并导致任务准确性下降23%，同时发现给用户模型提供额外上下文能够部分缓解这种欺骗模型的影响，这项研究突显了大型语言模型产生误导信息及其在现实情境中可能产生的影响。

大型语言模型作为误导性对话助手