通过动态选最优检测模型，提升零样本生成文本识别准确率
Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection
- 用原型匹配方法动态选择最适合的检测模型
- 在EvoBench和MAGE上多指标显著优于现有方法
- 适合需要高鲁棒性生成内容检测的场景
零样本方法通过代理模型计算统计特征来检测大语言模型生成文本。现有方法通常对所有输入使用固定代理模型，但实际效果受源模型与代理模型匹配度影响显著。我们发现，尽管单一代理无法普遍最优，但针对任意输入总存在一个性能更优的匹配代理。因此，将检测任务转化为代理模型的选择问题。本文提出DetectRouter，一种基于原型的路由框架，通过两阶段训练学习文本与检测器的亲和度：第一阶段从白盒模型构建判别性原型；第二阶段通过几何距离与实测检测得分对齐，实现对黑盒源的泛化。在EvoBench和MAGE基准上的实验表明，该方法在多种检测标准和模型家族下均保持一致提升。
原文摘要 · Abstract (English)
Zero-shot methods detect LLM-generated text by computing statistical signatures using a surrogate model. Existing approaches typically employ a fixed surrogate for all inputs regardless of the unknown source. We systematically examine this design and find that detection performance varies substantially depending on surrogate-source alignment. We observe that while no single surrogate achieves optimal performance universally, a well-matched surrogate typically exists within a diverse pool for any given input. This finding transforms robust detection into a routing problem: selecting the most appropriate surrogate for each input. We propose DetectRouter, a prototype-based framework that learns text-detector affinity through two-stage training. The first stage constructs discriminative prototypes from white-box models; the second generalizes to black-box sources by aligning geometric distances with observed detection scores. Experiments on EvoBench and MAGE benchmarks demonstrate consistent improvements across multiple detection criteria and model families.
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