DP-TabICL: In-Context Learning with Differentially Private Tabular Data

arXiv:2403.05681v1 Announce Type: cross
Abstract: In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have comparable performance to costly model retraining and fine-tuning. Recently, ICL has been extended to allow tabular data to be used as demonstration examples by serializing individual records into natural language formats. However, it has been shown that LLMs can leak information contained in prompts, and since tabular data …

abstract adapt arxiv context cs.ai cs.cr cs.lg data fine-tuning in-context learning language language models large language large language models llms model retraining performance question retraining tabular tabular data tasks type