Detection and Classification of AI-Generated Text
Shreya Gupta, Deepa Gupta · 2024
The advent of Large Language Models (LLMs), has largely transformed global interactions with artificial intelligence. These models, though useful in tasks ranging from translation to text generation, have now permeated everyday life, thereby raising concerns about their potential misuse, particularly in educational institutions. Despite measures like access restrictions implemented by educational institutions, AI based plagiarism has continued to grow, with existing plagiarism detection tools exhibiting limited efficacy in curbing the problem. This paper addresses the need for better AI detection algorithms, focusing on the detection of subtler forms of plagiarism facilitated by LLMs. Leveraging insights from existing studies, the research explores the efficacy of various boosting based machine learning models and dimensionality reduction techniques in improving the accuracy of detecting AI-augmented essays.