• Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors View PDF HTML (experimental)Abstract:High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared • Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have emerged as powerful engines for generating it • However, existing private text generation methods are severely inefficient: they are data-intensive, computationally slow, and often require large private corpora or batch sizes to achieve usable quality • We introduce EPSVec, a differentially-private lightweight alternative that steers LLM generation using dataset vectors–directions in activation space that capture the distributional gap between private data and public priors • EPSVec extracts and sanitizes steering vectors just once and then performs standard decoding • This decouples the privacy budget from generation, enabling arbitrarily many synthetic samples without additional privacy cost and yielding strong fidelity even in low-data regimes

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  • Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors View PDF HTML (experimental)Abstract:High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have emerged as powerful engines for generating it. However, existing private text generation methods are severely inefficient: they are data-intensive, computationally slow,

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