Powerful generative Large Language Models (LLMs) are becoming popular tools amongst the general public as question-answering systems, and are being utilised by vulnerable groups such as children. With children increasingly interacting with these tools, it is imperative for researchers to scrutinise the safety of LLMs, especially for applications that could lead to serious outcomes, such as online child safety queries. In this paper, the efficacy of LLMs for online grooming prevention is explored both for identifying and avoiding grooming through advice generation, and the impact of prompt design on model performance is investigated by varying the provided context and prompt specificity. In results reflecting over 6,000 LLM interactions, we find that no models were clearly appropriate for online grooming prevention, with an observed lack of consistency in behaviours, and potential for harmful answer generation, especially from open-source models. We outline where and how models fall short, providing suggestions for improvement, and identify prompt designs that heavily altered model performance in troubling ways, with findings that can be used to inform best practice usage guides.

翻译：强大的生成式大型语言模型（LLMs）正成为公众广泛使用的问答工具，并且被儿童等弱势群体所使用。随着儿童与这些工具的交互日益增多，研究人员必须严格审视LLMs的安全性，尤其是针对可能导致严重后果的应用场景，例如在线儿童安全咨询。本文探究了LLMs在在线诱骗预防中的有效性，包括通过建议生成来识别和避免诱骗行为，并通过改变提供的上下文和提示特异性，研究了提示设计对模型性能的影响。在反映超过6000次LLM交互的结果中，我们发现没有任何模型明确适用于在线诱骗预防，观察到行为一致性不足，以及可能生成有害答案（尤其是来自开源模型的问题）。我们概述了模型在哪些方面以及如何存在不足，提供了改进建议，并识别出以令人担忧的方式严重改变模型性能的提示设计，这些发现可用于制定最佳实践使用指南。