Privacy by Proximity: How Nearest Neighbors Made In-Context Learning Differentially Private
TL;DR for operators Private examples are not harmless just because they sit inside a prompt rather than inside model weights. In-context learning lets teams adapt a general LLM by adding examples at inference time, which is convenient until those examples are medical notes, legal clauses, customer tickets, invoices, or internal decisions that should not be inferable from the model’s output. ...
