ResearchNet: A Novel Framework for Detecting AI-Generated Scientific Content using SciBERT and Graph Neural Networks
Abstract
The fast development of Large Language Models is a problem for keeping academic integrity in scientific publishing. The usual tools that detect this kind of thing use statistics like perplexity and linguistic features. These tools demonstrate limited effectiveness against sophisticated domain-specific AIgenerated text. This paper presents ResearchNet, a hybrid detection framework for identifying whether scientific text is human-authored or LLM-generated. ResearchNet uses a kind of encoder called Frozen SciBERT and a Graph Convolutional Network, which looks at text as a graph where the sentences are connected by logical transitions. It also uses something called DeepScientificAttention to combine information about terminology and citations. The structure of the text to make a strong classification. ResearchNet was evaluated on the AIGTxt dataset across ten scientific fields such as Astrophysics, Medicine and Social Sciences ResearchNet achieves a ROC AUC of 90.02 percent and the highest Mixed-class F1 of 0.69 which is better than all the other models we compared it to and it was 2.7 percent points better, than the next best model, which was SciBERT+GCN.