arXiv Machine Learning
Aug 20

Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text

The paper introduces Pattern Stability Score (PSS), a watermark detection framework that uses local statistical features and stability dynamics across paraphrased variants to identify machine-generated text. PSS combines global and local z‑score features with higher‑order run‑length statistics, autocorrelation signals, and stability scores over paraphrase depth. Experiments on PG‑19, CNN/DailyMail, and WikiText with Llama‑3‑8B, Qwen2‑7B, and multiple paraphrasers show that PSS improves detection AUC by 10‑15 percentage points and a single universal classifier achieves over 87.8% AUC across diverse LLMs, paraphrasers, and domains without retraining.

By Sina Mansouri, Mohit Marvania, Abolfazl Safikhani
arXiv Machine Learning
Aug 11

UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers

arXiv:2608. 09209v1 Announce Type: cross Abstract: Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs.

By Chidaksh Ravuru, Shashank Srivastava