{"ID":2866864,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18535","arxiv_id":"2509.18535","title":"Trace Is In Sentences: Unbiased Lightweight ChatGPT-Generated Text Detector","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.","short_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, degr...","url_abs":"https://arxiv.org/abs/2509.18535","url_pdf":"https://arxiv.org/pdf/2509.18535v1","authors":"[\"Mo Mu\",\"Dianqiao Lei\",\"Chang Li\"]","published":"2025-09-23T02:00:35Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"eess.SP\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}