aiqbench analysis
ContinuousBench introduces a novel, automatically regenerated benchmark designed to measure genuine capability gains from differentially private synthetic text, addressing the saturation of existing evaluation metrics. The study reveals that while non-private synthesis effectively transfers knowledge from original corpora, current state-of-the-art differentially private methods generally fail to do so, even at high privacy budgets. This work provides a critical new standard for researchers aiming to validate whether privacy-preserving data synthesis can truly substitute access to sensitive original datasets.
ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
arXiv:2606.01849v2 Announce Type: replace Abstract: Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data transmits genuinely new knowledge and capabilities present only in those corpora. This is because existing evaluations rely on tasks that are nearly solvable without training, so strong benchmark performance does not establish that DP synthesis can substitute original data access. Thus, we introduce ContinuousBench, a continuously and automatically-regenerated benchmark that measures capability gain from DP synthetic text. Each quarter, a new release pairs a never-before-seen training corpus with a derived QA set, constructed to be: (1) unsolvable sans-corpus; and (2) learnable under DP, as the tested knowledge is supported by hundreds of independent records. Researchers produce DP synthetic data from the training corpus and run our standardized training and evaluation harness on their synthetic data to measure gains. We instantiate two tracks: Geminon, a procedurally-generated dataset about fictional creatures; and News, a stream of newly crawled public news articles. Although standard benchmarks are nearly saturated, on ContinuousBench we find that non-private synthesis transfers substantial knowledge from the original corpus, while state-of-the-art DP synthesis methods generally fail to do so, even at $\varepsilon=100$.
Title:ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
Abstract:Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data transmits genuinely new knowledge and capabilities present only in those corpora. This is because existing evaluations rely on tasks that are nearly solvable without training, so strong benchmark performance does not establish that DP synthesis can substitute original data access. Thus, we introduce ContinuousBench, a continuously and automatically-regenerated benchmark that measures capability gain from DP synthetic text. Each quarter, a new release pairs a never-before-seen training corpus with a derived QA set, constructed to be: (1) unsolvable sans-corpus; and (2) learnable under DP, as the tested knowledge is supported by hundreds of independent records. Researchers produce DP synthetic data from the training corpus and run our standardized training and evaluation harness on their synthetic data to measure gains. We instantiate two tracks: Geminon, a procedurally-generated dataset about fictional creatures; and News, a stream of newly crawled public news articles. Although standard benchmarks are nearly saturated, on ContinuousBench we find that non-private synthesis transfers substantial knowledge from the original corpus, while state-of-the-art DP synthesis methods generally fail to do so, even at $\varepsilon=100$.
Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
arXiv:2606.01849 [cs.LG]
(or arXiv:2606.01849v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.01849 Focus to learn more arXiv-issued DOI via DataCite
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