Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level

• URL: http://arxiv.org/abs/2306.08122v1
• Date: Tue, 13 Jun 2023 20:34:55 GMT
• Title: Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level
• Authors: Mujahid Ali Quidwai, Chunhui Li, Parijat Dube
• Abstract summary: Existing AI-generated text classifiers have limited accuracy and often produce false positives. We propose a novel approach using natural language processing (NLP) techniques. We generate multiple paraphrased versions of a given question and inputting them into the large language model to generate answers. By using a contrastive loss function based on cosine similarity, we match generated sentences with those from the student's response.
• Score: 4.250876580245865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism. Existing AI-generated text classifiers have limited accuracy and often produce false positives. We propose a novel approach using natural language processing (NLP) techniques, offering quantifiable metrics at both sentence and document levels for easier interpretation by human evaluators. Our method employs a multi-faceted approach, generating multiple paraphrased versions of a given question and inputting them into the LLM to generate answers. By using a contrastive loss function based on cosine similarity, we match generated sentences with those from the student's response. Our approach achieves up to 94% accuracy in classifying human and AI text, providing a robust and adaptable solution for plagiarism detection in academic settings. This method improves with LLM advancements, reducing the need for new model training or reconfiguration, and offers a more transparent way of evaluating and detecting AI-generated text.

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