Research on the Detection of Elements in AI Generation and Scholar Writing Papers
To explore the similarities and differences between AI and scholars in academic writings academic writing,three AI tools,ChatGPT,ERNIE Bot and Tongyi Qianwen were taken as research objects,and three different kinds of Chinese datasets were constructed for journal papers in the field of computer science,namely AI generation,scholars'writing,and the summa-ry,introduction,and conclusion texts of both.From the perspectives of part-of-speech tagging,text length,high-frequency words,and high-frequency collocations,a comparative analysis was conducted to evaluate the text quality of the dataset using indicators such as self-bleu,perplexity,and semantic similarity.We found that scholars typically use more complex sentence structures to write texts that exhibit higher diversity,while AI generated texts are more easily predicted by large language models.Subsequently,the detection task was transformed into a binary classification task and experiments were conducted on 13 baseline models.Furthermore,the DeBERTa-BiGRU model was proposed,and it can achieve an accuracy of 91％,which is superior to other classifiers.Through this innovative method,academic misconduct can be effectively prevented,providing de-tection tools for academic journal editors and maintaining the credibility of the academic community.
text classificationartificial intelligence generated contentdeep learningtext detection
NETL
NSTL
万方数据
