Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection

TL;DR

This paper introduces a multi-level contextual token relation modeling framework to improve the detection of machine-generated texts by addressing biases in token-level scores through local and global relation modeling.

Contribution

It presents a novel multi-level relation modeling approach that combines local calibration and global rule-support reasoning for more accurate MGT detection.

Findings

Significant performance improvements across multiple real-world scenarios.

Effective in cross-LLM and cross-domain detection tasks.

Low computational overhead compared to existing methods.

Abstract

Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which extract statistically distinguishable features of MGTs, are often more practical than complex model-based methods that are prone to overfitting. Given their diverse designs, we first place representative metric-based methods within a unified framework, enabling a clear assessment of their advantages and limitations. Our analysis identifies a core challenge across these methods: the token-level detection score is easily biased by the inherent randomness of the MGTs generation process. Then, we theoretically derive the multi-hop transitions of the token-level detection score and explore their local and global relations. Based on these findings, we propose a multi-level contextual token relation modeling framework for MGT detection. Specifically, for local relations, we model them through a lightweight Markov-informed calibration module that refines token-level evidence before aggregation. For global relations, we introduce a rule-support reasoning module that uses explicit logical rules derived from contextual score statistics. Finally, we combine the local calibrated score and the global rule-support reasoning signal in a joint multi-level inference framework. Extensive experiments show broad and substantial improvements across various real-world scenarios, including cross-LLM and cross-domain settings, with low computational overhead.