DistilBERT: A Novel Approach to Detect Text Generated by Large Language Models (LLM)

Abstract

<title>Abstract</title> Large language models (LLMs) have emerged as powerful tools for generating human-quality text, raising concerns about their potential for misuse in academic settings. This paper investigates the use of DistilBERT, a distilled version of BERT, for detecting LLM-generated text. We evaluate its performance on two publicly available datasets, LLM-Detect AI Generated Text and DAIGT-V3 Train Dataset, achieving an average accuracy of around 94%. Our findings suggest that DistilBERT is a promising tool for safeguarding academic integrity in the era of LLMs.