通过思维链推理识别文本生成模型，提升可解释性。
Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text
- 用思维链推理辅助判断文本是否为AI生成
- 能准确识别出具体使用的语言模型
- 适合需要透明化AI检测的场景
近年来，由于对学术诚信、虚假信息和伦理AI部署的担忧，识别AI生成文本成为研究热点。本文提出COT Fine-tuned框架，用于检测AI生成文本并识别其背后的特定语言模型。该方法采用双任务设计：任务A为区分文本是AI生成还是人工撰写，任务B则识别具体的语言模型。核心创新在于引入思维链（Chain-of-Thought）推理，使模型在预测时生成解释，增强结果的透明度与可解释性。实验表明，COT Fine-tuned在两项任务中均表现优异，尤其在语言模型识别和人机分类上效果显著，且思维链推理过程显著提升了模型性能与可解释性。
原文摘要 · Abstract (English)
In recent years, the detection of AI-generated text has become a critical area of research due to concerns about academic integrity, misinformation, and ethical AI deployment. This paper presents COT Fine-tuned, a novel framework for detecting AI-generated text and identifying the specific language model. responsible for generating the text. We propose a dual-task approach, where Task A involves classifying text as AI-generated or human-written, and Task B identifies the specific LLM behind the text. The key innovation of our method lies in the use of Chain-of-Thought reasoning, which enables the model to generate explanations for its predictions, enhancing transparency and interpretability. Our experiments demonstrate that COT Fine-tuned achieves high accuracy in both tasks, with strong performance in LLM identification and human-AI classification. We also show that the CoT reasoning process contributes significantly to the models effectiveness and interpretability.
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