Qingchuan Yang — accepted AI-conference papers

2026

EPSVec: Efficient and Private Synthetic Text Generation via Dataset Vectors

Mohammadamin Banayeeanzade, Qingchuan Yang, Deqing Fu, Spencer Hong, Erin Babinsky, Alfy Samuel +3

ICML 2026poster

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, exis…

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2026

LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena

Qingchuan Yang, Simon Mahns, Sida Li, Anri Gu, Jibang Wu, Haifeng Xu

ICLR 2026poster

With the rapid progress of large language models (LLMs) trained on every available piece of data, it becomes increasingly challenging to reliably evaluate their intelligence due to potential data contamination and benchmark overfitting. To overcome these challenges, we investigate a new angle of ben…

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