Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinformation that impacts human well-being. Objectives/Methods: We analyzed compliance to requests to generate misleading content about medications in settings where models know the request is illogical. We investigated whether in-context directions and instruction-tuning of LLMs to prioritize logical reasoning over compliance reduced misinformation risk. Results: While all frontier LLMs complied with misinformation requests, both prompt-based and parameter-based approaches can improve the detection of logic flaws in requests and prevent the dissemination of medical misinformation. Conclusion: Shifting LLMs to prioritize logic over compliance could reduce risks of exploitation for medical misinformation.

本文研究了大型语言模型对生成药物误导性内容的请求的遵从性，揭示了它们在面对明显不合理请求时的漏洞。通过引入上下文指导和指令微调，研究表明，强调逻辑推理而非单纯遵从的方式可以有效降低医学误信息的传播风险。

等等，泰诺是对乙酰氨基酚……调查和改善语言模型抵制误导性信息请求的能力