Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text
This addresses concerns about academic integrity and misinformation by providing a transparent method for AI text detection, though it is incremental as it builds on existing detection techniques.
The paper tackles the problem of detecting AI-generated text and identifying the specific language model behind it, achieving high accuracy in both tasks through a novel framework that uses Chain-of-Thought reasoning.
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.