RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts

• URL: http://arxiv.org/abs/2402.14838v1
• Date: Mon, 19 Feb 2024 00:40:17 GMT
• Title: RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts
• Authors: Mohammad Heydari Rad, Farhan Farsi, Shayan Bali, Romina Etezadi, Mehrnoush Shamsfard
• Abstract summary: This article investigates the problem of AI-generated text detection from two different aspects: semantics and syntax. We present an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks.
• Score: 0.8437187555622164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.

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