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.

本研究针对AI生成文本的检测，提出了COT Fine-tuned框架，通过双任务方法改善文本的识别准确性。创新之处在于运用思维链推理，使模型生成其预测的解释，从而提升透明度与可解释性，实验表明该方法在AI与人类文本分类及LLM识别方面表现出色。

追踪思维：利用思维链推理识别AI生成文本背后的语言模型