用可解释推理提升AI文本检测准确率，小模型超越大模型。
READER: Reasoning-Enhanced AI-Generated Text Detection
- 通过推理机制让模型先分析证据再判断
- 15亿参数模型在多场景下优于千倍大的模型
- 输出带理由的判断，适合需要可信检测的场景
大型语言模型的进步使得区分人工写作与AI生成内容愈发困难。现有检测器多采用监督训练的神经分类器，在分布内表现良好但缺乏透明性，且在分布外性能显著下降。本文提出READER，一种基于推理增强的AI文本检测器，能输出人类/机器标签及结构化推理依据。核心是构建了包含推理过程和判断结果的精选标注集READ。通过在READ上微调一个15亿参数的LLM，得到具备推理能力的READER，在推理时先分析证据再做决策。尽管仅1.5B参数，其性能持续超越现有检测器及多个高容量提示模型（如GPT-5.2、Gemini-3-Pro、DeepSeek-V3.2），后者规模为前者的100至1000倍。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have made it increasingly difficult to distinguish human-written text from AI-generated content. Many existing detectors train supervised neural classifiers that achieve strong in-distribution performance but are often opaque and can degrade substantially under distribution shift. We present READER, a reasoning-enhanced AI text detector that outputs both a human/AI label and a structured rationale describing the evidence for its decision. A key component of our approach is READ, a curated supervision set of rationales and verdicts. We fine-tune an LLM on READ to build READER, which reasons before detecting at inference time. Despite having only 1.5B parameters, READER consistently outperforms existing detectors as well as prompted, high-capacity LLM baselines (GPT-5.2, Gemini-3-Pro, and DeepSeek-V3.2), which are 100 to 1000 times larger in scale.
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