Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets. An adversary can exploit this tendency to extract training data. Depending on the nature of the content and the context in which this data was collected, this could violate expectations of privacy. Thus there is a growing interest in techniques for training language models that preserve privacy. In this paper, we discuss the mismatch between the narrow assumptions made by popular data protection techniques (data sanitization and differential privacy), and the broadness of natural language and of privacy as a social norm. We argue that existing protection methods cannot guarantee a generic and meaningful notion of privacy for language models. We conclude that language models should be trained on text data which was explicitly produced for public use.

翻译：语言模式缺乏理解文字背景和敏感性的能力,而且倾向于将培训组合中的语句混为一谈。对手可以利用这种获取培训数据的趋势。视内容的性质和收集这种数据的背景而定,这可能违反对隐私的预期。因此,对培训语言模式保护隐私的技术的兴趣日益浓厚。在本文中,我们讨论了大众数据保护技术(数据清洁化和差异隐私)的狭隘假设与自然语言和隐私作为社会规范的广泛性之间的不匹配。我们认为,现有的保护方法不能保证语言模型的隐私概念具有通用和有意义的意义。我们的结论是,语言模型应当就明确为公众使用而制作的文本数据进行培训。