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Computer Science > Computation and Language

arXiv:2312.10617 (cs)
[Submitted on 17 Dec 2023]

Title:Deep dive into language traits of AI-generated Abstracts

Authors:Vikas Kumar, Amisha Bharti, Devanshu Verma, Vasudha Bhatnagar
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Abstract: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.
Comments: Accepted for Cods-Comad Conference
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2312.10617 [cs.CL]
  (or arXiv:2312.10617v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2312.10617

Submission history

From: Devanshu Verma [view email]
[v1] Sun, 17 Dec 2023 06:03:33 UTC (377 KB)
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