Deep dive into language traits of AI-generated Abstracts
Vikas Kumar Amisha Bharti Devanshu Verma Vasudha Bhatnagar
Generative language models, such as ChatGPT, have garnered attention for
their ability to generate human-like writing in various fields, including
academic research. The rapid proliferation of generated texts has bolstered the
need for automatic identification to uphold transparency and trust in the
information. However, these generated texts closely resemble human writing and
often have subtle differences in the grammatical structure, tones, and
patterns, which makes systematic scrutinization challenging. In this work, we
attempt to detect the Abstracts generated by ChatGPT, which are much shorter in
length and bounded. We extract the texts semantic and lexical properties and
observe that traditional machine learning models can confidently detect these
Abstracts.