Author

Evan Frick

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#artificial intelligence Preprint Oct 2026

High-Value Training Data from Noisy, Diverse Sources

Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Dataweighting---assigning different weights to training examples---offers a general way to control how such data influences training, but learni...

Hedi Driss, Evan Frick, Anastasios Nikolas Angelopoulos et al. · 0 citations

Jun 2026

Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist

Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, on which top-tier systems already achieve near-perfect scores. As T2I models enter cre...

Yuanhao Ban, Tong Xie, Sohyun An et al. · arXiv.org · 0 citations