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pinterest LLM-Powered Relevance Assessment for Pinterest Search -- 2 Listen Share Han Wang | Machine Learning Engineer; Alex Whitworth | Staff Data Scientist; Pak Ming Cheung | Sr. Staff Machine Learning Engineer; Zhenjie Zhang | Sr. Staff Machine Learning Engineer Introduction Search rel…
google Researchers at Google have developed an inference-only method for generating differentially private (DP) synthetic data that avoids the high costs and data requirements associated with private fine-tuning. By prompting off-the-shelf large language models (LLMs) with sensitive examples in parallel and aggregating their outputs, the approach can generate thousands of high-quality synthetic data points while maintaining rigorous privacy guarantees. This method allows synthetic data to serve as a secure interface for model development, enabling teams to collaborate without requiring specialized knowledge of differential privacy. ## Differentially Private Prediction and Aggregation The core of this method relies on "private prediction," where privacy is applied to the model's output rather than the model itself. * Sensitive data points are distributed across multiple independent prompts, ensuring that no single individual's record can significantly influence the final output. * The LLM generates next-token predictions for each prompt in parallel, which are then aggregated to mask individual contributions. * The researchers designed a DP token sampling algorithm that treats the standard LLM "softmax" sampling process as a version of the exponential mechanism, a mathematical framework used to select the best option from a set while maintaining privacy. ## Enhancing Efficiency via KV Caching Previous attempts at private prediction were computationally expensive because they required a fresh batch of sensitive examples for every single token generated. * A new privacy analysis allows the system to reuse a fixed batch of sensitive examples across an entire generation sequence. * By maintaining the same context for each generation step, the system becomes compatible with standard inference optimization techniques like KV (Key-Value) caching. * This improvement enables the generation of synthetic data at a scale two to three orders of magnitude larger than prior methods. ## Optimizing Privacy Spend with Public Drafters To preserve the "privacy budget"—the limited amount of information that can be released before privacy is compromised—the method introduces a public drafter model. * The drafter model predicts the next token based solely on previously generated synthetic text, without ever seeing the sensitive data. * Using the sparse vector technique, the system only consumes the privacy budget when the public drafter’s suggestion disagrees with the private aggregate of the sensitive data. * This is particularly useful for structured data, where the drafter can handle formatting and syntax tokens, saving the privacy budget for the actual content. By leveraging off-the-shelf models like Gemma, this approach provides a scalable way to transform sensitive datasets into useful synthetic versions. These synthetic datasets are high-quality enough to replace real data in downstream machine learning tasks, such as in-context learning or fine-tuning models like BERT, without the risk of leaking individual user information.