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arXiv · 2509.18535

Trace Is In Sentences: Unbiased Lightweight ChatGPT-Generated Text Detector

Mo Mu·Dianqiao Lei·Chang Li
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Abstract

The widespread adoption of ChatGPT has raised concerns about its misuse, highlighting the need for robust detection of AI-generated text. Current word-level detectors are vulnerable to paraphrasing or simple prompts (PSP), suffer from biases induced by ChatGPT's word-level patterns (CWP) and training data content, degrade on modified text, and often require large models or online LLM interaction. To tackle these issues, we introduce a novel task to detect both original and PSP-modified AI-generated texts, and propose a lightweight framework that classifies texts based on their internal structure, which remains invariant under word-level changes. Our approach encodes sentence embeddings from pre-trained language models and models their relationships via attention. We employ contrastive learning to mitigate embedding biases from autoregressive generation and incorporate a causal graph with counterfactual methods to isolate structural features from topic-related biases. Experiments on two curated datasets, including abstract comparisons and revised life FAQs, validate the effectiveness of our method.

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Mo Mu, Dianqiao Lei, Chang Li. 2025-09-23. Trace Is In Sentences: Unbiased Lightweight ChatGPT-Generated Text Detector. https://arxiv.org/abs/2509.18535

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