arXiv AI By Xiaoyang Han, Lvxiaowei Xu, Ming Cai

When Less is More: Understanding When Token Filtering Helps and Fails in AI-generated Text Detection

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arXiv Machine Learning
Sep 7

Single-Query Black-Box Calibration Auditing via Logit Bias

The paper introduces a method for auditing the calibration of large language models (LLMs) that only exposes a logit_bias parameter. By mathematically manipulating this parameter, the authors can evaluate exact probability thresholds with a single query per sample, enabling a provably consistent estimator of True Calibration Error for binary tasks. This approach offers an efficient framework for auditing black‑box foundation models despite limited access to continuous output probabilities.

By Roman Plaud, Antoine Saillenfest, Matthieu Labeau, Thomas Bonald, Willem Waegeman
arXiv AI
Jul 24

Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.

By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu