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New York University, University of Amsterdam
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LLMs can significantly boost the utility of differentially private de-identification for clinical text, offering a path to better privacy-preserving data sharing.
Instruction-tuned MLLMs, despite excelling in zero-shot scenarios, surprisingly fail to leverage few-shot examples or chain-of-thought prompting, suggesting a fundamental limitation in their ability to learn from demonstrations.
