Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912343504453632 |
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| author | Agrahari, Shifali Singh, Sanasam Ranbir |
| author_facet | Agrahari, Shifali Singh, Sanasam Ranbir |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_16913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text Agrahari, Shifali Singh, Sanasam Ranbir Computation and Language Artificial Intelligence 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. |
| title | Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.16913 |