通过文本重写差异检测大模型生成内容，效果优于现有方法。
Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection
- 利用人类文本与大模型重写版本的差异性进行检测
- 在多个模型和语言上实现最高34.3%的AUROC提升
- 无需白盒假设，适合实际场景应用
检测大语言模型（LLMs）生成的文本至关重要但极具挑战。现有检测器依赖不切实际的假设（如白盒设置），或仅依赖文本层面特征，导致检测精度不足。本文提出一种简单但高效且实用的检测方法VaryBalance。其核心思想是：相比大模型生成文本，人类文本与其经大模型重写后的版本之间差异更大。基于此，VaryBalance通过均值标准差量化该差异，并实现对人类文本与大模型生成文本的有效区分。大量实验证明，VaryBalance在多个数据集上优于当前最优检测器Binoculars，AUROC最高提升达34.3%，且对多种生成模型和语言具有强鲁棒性。
原文摘要 · Abstract (English)
Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this paper, we propose a simple but effective and practical LLM-generated text detection method, VaryBalance. The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their rewritten version via LLMs. Leveraging this observation, VaryBalance quantifies this through mean standard deviation and distinguishes human texts and LLM-generated texts. Comprehensive experiments demonstrated that VaryBalance outperforms the state-of-the-art detectors, i.e., Binoculars, by up to 34.3\% in terms of AUROC, and maintains robustness against multiple generating models and languages.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。