[2603.19258] MAPLE: Metadata Augmented Private Language Evolution

Computer Science > Computation and Language arXiv:2603.19258 (cs) [Submitted on 26 Feb 2026] Title:MAPLE: Metadata Augmented Private Language Evolution Authors:Eli Chien, Yuzheng Hu, Ryan McKenna, Shanshan Wu, Zheng Xu, Peter Kairouz View a PDF of the paper titled MAPLE: Metadata Augmented Private Language Evolution, by Eli Chien and 5 other authors View PDF HTML (experimental) Abstract:While differentially private (DP) fine-tuning of large language models (LLMs) is a powerful tool, it is often computationally prohibitive or infeasible when state-of-the-art models are only accessible via proprietary APIs. In such settings, generating DP synthetic data has emerged as a crucial alternative, offering the added benefits of arbitrary reuse across downstream tasks and transparent exploratory data analysis without the opaque constraints of a model's parameter space. Private Evolution (PE) is a promising API-based framework for this goal; however, its performance critically depends on initialization. When the private data distribution deviates substantially from the foundation model's pre-training priors--particularly in highly specialized domains--PE frequently struggles to align with the target data, resulting in degraded utility, poor convergence, and inefficient API usage. To address this initialization bottleneck, we propose Metadata Augmented Private Language Evolution (MAPLE). MAPLE leverages differentially private tabular metadata extraction and in-context learning to effe...