想查一段文字是不是AI写的？这个检测器能看到词级线索，准确率97%以上，还公开代码和数据集，可自己跑。
DeBERTa-Sentinel 基于 DeBERTa-v3 的解纠缠注意力，用于识别 AI 生成的文本。研究者在 GLC-AIText 数据集的 28,057 条 GPT、LLaMA、Claude 样本上以 60-20-20 划分训练，验证准确率 98.21%。该模型测试准确率 97.53%、召回率 99.33%、ROC-AUC 99.53%，超过 NeurIPS 2025 的 RoBERTa-Sentinel 基线。与黑盒检测器不同，它提供 token 级解释，帮助记者、教育工作者和平台审核人员审计检测结果。
DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text
The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation, and risks to vulnerable online communities. Existing transformer-based detectors, such as GPT-Sentinel, show promise but struggle to generalize to diverse model outputs and paraphrasing attacks, limiting their role in building trustworthy web ecosystems. This work introduces DeBERTa-Sentinel, a responsible AI-generated text detection framework leveraging DeBERTa-v3's disentangled attention to capture subtle structural irregularities in synthetic content. A central design principle is transparency: unlike black-box commercial detectors, DeBERTa-Sentinel exposes token-level explanations of its decisions, enabling affected stakeholders journalists, educators, and platform trust and safety teams to audit, challenge, and contextualize detection outcomes. Using the GLC-AIText dataset of 28,057 human and LLM-generated samples (GPT, LLaMA, and Claude) with a 60-20-20 split, DeBERTa-Sentinel achieves 98.21\% validation accuracy and surpasses the RoBERTa-Sentinel baseline from NeurIPS 2025, achieving 97.53\% test accuracy, 95.89\% precision, 99.33\% recall, and 99.53\% ROC-AUC, and maintaining a 0.665\% false negative rate. The model's interpretability reveals linguistic markers such as academic phrasing and formal transitions associated with synthetic text, directly supporting stakeholder needs for verifiable, auditable content-authenticity decisions. By advancing responsible detection methods that reduce bias and enhance explainability, DeBERTa-Sentinel promotes trustworthy, ethical, and human-centric AI systems. Code and data are available at https://github.com/Galileo-Galili/HUMAN-VS-AI-TEXT-DETECTION.