Societal Impact of Large Language Models
Large language models have raised several concerns related to privacy. These models are trained on vast amounts of data, including personal information, and can generate highly accurate predictions about individuals. As a result, there is a risk that large language models could be used to violate privacy rights.
One concern is that large language models could be used to re-identify individuals from supposedly anonymous data. For example, an attacker could use a language model to identify a person from a supposedly anonymous medical record by matching the text with other available information. This could lead to sensitive information being exposed or used for malicious purposes.
Another concern is that large language models could be used to infer sensitive information about individuals, such as their sexual orientation, political views or mental health status. This could have serious consequences for individuals, such as discrimination or stigmatization.
Moreover, large language models raise concerns about the security of personal information. These models are often trained on data that is stored in the cloud, which could be vulnerable to hacking or other security breaches. If an attacker gains access to a language model, they could potentially use it to infer sensitive information about individuals.
To address these concerns, several researchers have proposed methods to increase privacy when training and using large language models. For example, differential privacy can be used to add noise to training data, making it harder for an attacker to infer sensitive information. Federated learning can also be used to train models on data that is distributed across multiple devices, without compromising the privacy of individual users.
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