# CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning Source: [CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:52:52.049Z Authors: Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Hang Pu, Yu Lan, Chao Shen Published: 2022-12-20T15:26:19.000Z ## Abstract Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently. Latest proposed detectors usually take coarse text sequences as input and fine-tune pretrained models with standard cross-entropy loss. However, these methods fail to consider the linguistic structure of texts. Moreover, they lack the ability to handle the low-resource problem which could often happen in practice considering the enormous amount of textual data online. In this paper, we present a coherence-based contrastive learning model named CoCo to detect the possible MGT under low-resource scenario. To exploit the linguistic feature, we encode coherence information in form of graph into text representation. To tackle the challenges of low data resource, we employ a contrastive learning framework and propose an improved contrastive loss for preventing performance degradation brought by simple samples. The experiment results on two public datasets and two self-constructed datasets prove our approach outperforms the state-of-art methods significantly. Also, we surprisingly find that MGTs originated from up-to-date language models could be easier to detect than these from previous models, in our experiments. And we propose some preliminary explanations for this counter-intuitive phenomena. All the codes and datasets are open-sourced. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Machine-generated text detection on MGTEVAL SemEval 2024 human text & Qwen3 machine-generated text 1.0 (test)](): Machine-generated text detection; MGTEVAL SemEval 2024 human text & Qwen3 machine-generated text 1.0 (test); Accuracy: 97.5. ## Original sources and implementation [Original paper]() [PDF]()