Detecting AI-Generated Scientific Abstracts Using Galactica and Graph Neural Networks

The rise of large language models has introduced new challenges in maintaining research integrity, particularly through the potential proliferation of AI-generated scientific content. This paper presents a novel hybrid framework that combines Galactica-a scientific language model developed by Meta AI-with Graph Neural Networks (GNNs) to detect AI-generated research abstracts. Leveraging the AI-GA dataset comprising 28,662 labeled abstracts, we extract domain-specific semantic embeddings using Galactica and construct a semantic similarity graph to approximate citation-like relationships. A two- layer GCN is then trained to classify each abstract as human- or AI-authored. Experimental results demonstrate that our method outperforms traditional baselines such as TF-IDF, RoBERTa, and perplexity-based detectors, while offering interpretable and scalable detection suitable for editorial screening pipelines.