The rise of large language models (LLMs) has transformed scientific research but also enabled
misuse, with papers increasingly containing AI-generated or paraphrased text. To ensure fair and
transparent policies, reliable methods for detecting AI-produced content are essential.
The primary objective of this project is to apply advanced AI techniques, including machine learning, large language models (LLM), and natural language processing (NLP), to distinguish AI-produced scientific content from human-written.
The intern will develop an AI-detector, which will be evaluated on existing data using a defined evaluation pipeline. The focus of this project will be to employ AI agents that will evaluate a text from different perspectives, in order to determine if a text excerpt is human or AI-written.
Expected results:
This project will develop an enhanced AI content detector that builds on prior research and
leverages state-of-the-art methods. Beyond standard classifiers, it will explore Agentic AI and
Retrieval-Augmented Generation (RAG) to boost robustness and contextual accuracy. The
system will be tested on public and proprietary datasets, providing a stronger baseline for
detecting AI writing in scientific publishing
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