Deep dive into language traits of AI-generated Abstracts
This addresses the need for transparency in academic publishing by providing a detection method for AI-generated content, though it is incremental as it applies existing techniques to a new domain.
The paper tackled the problem of detecting AI-generated academic abstracts, finding that traditional machine learning models can confidently identify them based on semantic and lexical properties.
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.