Detection of LLM-Generated Text vs. Human Text via DeepSeek-R1 Multi-Feature Fusion

The development of generative artificial intelligence technology has brought convenience to various industries, but also caused some confusion. Especially today, when the content generated by large language models is extremely similar to real text, it has created challenges in many fields (such as in the discrimination of graduation theses in schools) in quickly identifying whether a text is from human sources or generated by large language models. Based on the DeepSeek-R1 language model, this paper combines natural language features and uses a judgment mechanism to detect text generated by large language models. Experimental results show that its accuracy is improved compared with conventional methods in the Reuters, WP and HC3 datasets.