联邦学习下用合成表格数据自我优化大模型，Concordia框架有望提升隐私与效率。
arXiv:2605.09855v2 Announce Type: replace Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, bu…
利用社会对齐的合成数据，让AI评估更贴近真实社会场景，提升模型敏感性与可信度。
arXiv:2605.14381v2 Announce Type: replace Abstract: Recent advancements in generative AI facilitate large-scale synthetic data generation for model ev…
用LLM引导潜在扩散模型，保留合成表格数据的列间逻辑关系，提升数据真实性和可用性。
arXiv:2503.02161v3 Announce Type: replace Abstract: Synthetic tabular data are increasingly being used to replace real data, serving as an effective s…
针对医疗健康表格数据，提出编排与评估合成数据的系统化框架，填补数据隐私与可用性平衡的空白。
arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but w…
新论文提出CasualSynth，教你生成结构合理的合成数据，数据科学必看
arXiv:2605.17528v1 Announce Type: new Abstract: Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outpu…
系统性综述揭示合成表格健康数据评估的关键挑战与指南，为数据质量评估提供严谨框架
arXiv:2504.18544v3 Announce Type: replace-cross Abstract: Generating synthetic tabular health data is challenging, and evaluating their quality is equ…
利用熵最优传输实现几何约束的冠状动脉狭窄编辑，为医学影像数据增强提供新思路，有望突破数据稀缺瓶颈，提升自动检测的泛化能力。
arXiv:2605.08851v2 Announce Type: replace-cross Abstract: The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the…