arXiv:2606. 13629v1 Announce Type: cross Abstract: There is a proliferation of work arguing for the use of synthetic data in scientific research.
By Lezhi Tan, Tijana Zrnic
The paper introduces a framework for synthetic‑augmented inference that balances the number of synthetic observations with their assigned weight. It defines a size‑weight frontier, estimating for each weight the maximum synthetic sample size that still guarantees target task‑marginal coverage for all smaller sizes. The authors provide finite‑sample coverage guarantees for configurations on or below this frontier and demonstrate that, when applied to augment opinion survey data with large language model responses, the method achieves the desired coverage while significantly tightening confidence intervals.
By Chengpiao Huang, Kaizheng Wang
The paper introduces a conformal prediction framework designed for molecular property prediction under label shift. By weighting conformal scores with marginal label probability ratios, it generates statistically rigorous prediction intervals without retraining, enabling robust uncertainty quantification when property distributions change. This approach provides actionable confidence measures that improve the reliability of AI-driven predictions in drug discovery.
By Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi, Hyunjin Shin
arXiv:2610.00814v1 Announce Type: cross
Abstract: Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data m...
By Yang Ba, Michelle V. Mancenido, Rong Pan
arXiv:2606. 06724v1 Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation.
By Jari Veps\"al\"ainen
GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.
By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar