2 citations · 2 across the 8 of their papers we have counts for
8 papers
CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
Jeffrey G. Wang, Jason Wang, Marvin Li +1
Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties. Although several attempts have been made to evaluate MIAs on langua…
ERRORQUAKE: Heavy-Tailed Error Severity Distributions in Open-Weight Large Language Models
Jason Z Wang
At matched accuracy, open-weight LLMs differ substantially in the shape of their error severity distribution -- a difference invisible to the scalar error rate. Hallucination bench…
The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models
Jason Z Wang
We give a stereological theory of LLM benchmark coverage. For any suite with effective dimensionality d_eff, the visible Hausdorff distance between two convex capability profiles c…
The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime
Jason Z Wang
The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical noise floor. We prove this i…
Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM
YuanLab. ai, :, Shawn Wu +25
We introduce Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 68.8B activated parameters and 1010B total parameters, specially designed to enha…
Hardness of Learning Neural Networks under the Manifold Hypothesis
Bobak T. Kiani, Jason Wang, Melanie Weber
The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding geometric structure has been demonstrated empi…
Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models
Jeffrey G. Wang, Jason Wang, Marvin Li +1
In this paper we develop state-of-the-art privacy attacks against Large Language Models (LLMs), where an adversary with some access to the model tries to learn something about the…
MoPe: Model Perturbation-based Privacy Attacks on Language Models
Marvin Li, Jason Wang, Jeffrey Wang +1
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations…