In today’s digital landscape, the widespread use of large language models has made it increasingly difficult to differentiate between human-written and AI-generated text. This challenge arises because state-of-the-art LLMs like Gemini, GPT series including ChatGPT GPT-4, and LLaMa produce highly sophisticated, human-like text. This indistinguishability poses various problems across various sectors, including cybersecurity threats, the generation of propaganda, the dissemination of biased or false information on social media, and facilitating social engineering attacks. In the educational domain, these models contribute to academic dishonesty, while in complex, multi-team environments, they add new layers of intricacy in managing human-AI interactions. All source code is available in my GitHub repository ( https://github.com/mohanreddy91/AI-Generated-Text-Detection .).

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Automatic Detection of AI-Generated Text from LLMs Using Feature-Driven Transformer Networks

  • Annepaka Yadagiri,
  • Reddi Mohana Krishna,
  • Partha Pakray,
  • Matus Pleva,
  • Daniel Hladek,
  • Kristian Sopkovic

摘要

In today’s digital landscape, the widespread use of large language models has made it increasingly difficult to differentiate between human-written and AI-generated text. This challenge arises because state-of-the-art LLMs like Gemini, GPT series including ChatGPT GPT-4, and LLaMa produce highly sophisticated, human-like text. This indistinguishability poses various problems across various sectors, including cybersecurity threats, the generation of propaganda, the dissemination of biased or false information on social media, and facilitating social engineering attacks. In the educational domain, these models contribute to academic dishonesty, while in complex, multi-team environments, they add new layers of intricacy in managing human-AI interactions. All source code is available in my GitHub repository ( https://github.com/mohanreddy91/AI-Generated-Text-Detection .).