We introduce the framework of "social learning" in the context of large language models (LLMs), whereby models share knowledge with each other in a privacy-aware manner using natural language. We present and evaluate two approaches for knowledge transfer between LLMs. In the first scenario, we allow the model to generate abstract prompts aiming to teach the task. In our second approach, models transfer knowledge by generating synthetic examples. We evaluate these methods across diverse datasets and quantify memorization as a proxy for privacy loss. These techniques inspired by social learning yield promising results with low memorization of the original data. In particular, we show that performance using these methods is comparable to results with the use of original labels and prompts. Our work demonstrates the viability of social learning for LLMs, establishes baseline approaches and highlights several unexplored areas for future work.

翻译：我们提出了大语言模型（LLMs）背景下"社会学习"的框架，该框架使模型能够以隐私保护的方式通过自然语言相互共享知识。我们提出并评估了两种大语言模型间的知识迁移方法。第一种方案允许模型生成旨在教授任务的抽象提示，第二种方案则通过合成示例实现知识迁移。我们在多样化数据集上评估了这些方法，并通过量化记忆性作为隐私损失的代理指标。受社会学习启发的这些技术取得了令人瞩目的成果，同时原始数据记忆度极低。特别值得注意的是，这些方法的性能与使用原始标签和提示词的结果相当。本研究表明了大语言模型社会学习的可行性，建立了基准方法，并指出了未来值得探索的研究空白。