LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection

ACL ARR 2026 January Submission1122 Authors

Keywords: machine-revised text detection, momentum contrastive learning, length-aware hard negative sampling

Abstract: Detecting machine-revised text that exhibits subtle lexical differences from the original human-generated text remains a challenge. Recent detection methods, including watermarking-based, logit-based, and training-based models, struggle to capture the fine-grained semantic differences, especially for short texts. To address this issue, we propose Length-aware Momentum Contrastive Learning (LAMCL), a novel framework for multiscale machine-revised text detection that integrates two core modules. To enhance the discriminative semantic features, the Enhance Before Detection (EBD) module first fuses the original detected text with the counterpart processed by a Large Language Model (LLM), and then measures semantic consistency to distinguish between machine-revised and human-generated text. Meanwhile, based on the Momentum Contrastive Learning (MCL) framework, the Length-aware Weighting (LW) module leverages text length and label information for hard negative sampling, mitigating the ambiguity of short text attribution and boosting the robustness of representation learning. Experimental results demonstrate that our method outperforms the existing detectors in identifying multiscale machine-revised text across diverse practical scenarios, tasks, and LLMs.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: self-supervised learning, contrastive learning, data augmentation

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English

Submission Number: 1122