Data Privacy in LLMs

Can you help break down the “data privacy” issue related to LLMs ?

I can see that there’s an issue of making the model forget some data set that it was used to be pre-trained with.

I don’t see any issue of “sharing” private data in a few shot prompting. Because the model won’t retain that data. Or is it not?

Other than that, if some private data was used in Pre-training; it can be an issue.

Sure, I can help break down the “data privacy” issue related to LLMs.

LLMs are large language models that use machine learning to generate text. One of the main concerns related to data privacy is that LLMs can be pre-trained on large datasets, some of which may contain private data. If this is the case, then there is a risk that the model could learn to generate text that reveals private information.

Another issue is that LLMs can be fine-tuned on smaller datasets, which may also contain private data. In this case, there is a risk that the model could memorize the private data and use it to generate text.

However, as you mentioned, there are ways to mitigate these risks. For example, it is possible to make the model forget some of the data it was pre-trained with. Additionally, if private data is used in few-shot prompting, the model may not retain that data.

Hi I think the issue here is that LLMs are trained on vast datasets from the Internet, that may contain publicly accessible IP without the rightful owner’s permission (e.g. people uploading PDF textbooks to some publicly exposed environment) .

The effort mentioned in the course seems to relate to the effort of suppressing the LLM’s ability to recall the associated information, in order to limit legal exposure to the rights holders.

Thanks for the replies.
Yes, other than the models using data that has not been cleared for PII for training - either for the base model or for the fine-tuning; I can’t see any data privacy issue.

Of course! Here are the two key points about data privacy related to Large Language Models (LLMs) like GPT-3.5:

  1. Pre-training and Data Retention: During the initial training, the LLM learns from a vast amount of data to understand language and general knowledge. The problem is that it might remember sensitive information from that data. For example, if the model is trained on a lot of medical records, it could potentially retain private patient information, which is not good for privacy.
  2. Fine-tuning and Data Leakage: After the initial training, the LLM undergoes more specific training on particular tasks or topics. If this fine-tuning data contains private or sensitive information, there’s a risk that the model might accidentally reveal that information. For instance, if the model is fine-tuned using personal messages or financial data, it could unintentionally leak those details when generating responses.
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