1 citations · 1 across the 7 of their papers we have counts for
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Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing
Yuchen Guo, Zhicheng Dou, Huy H. Nguyen +3
Content creation has dramatically progressed with the rapid advancement of large language models like ChatGPT and Claude. While this progress has greatly enhanced various aspects o…
Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias
Timo Spinde, Luyang Lin, Smi Hinterreiter +1
This paper introduces TaxoMatic, a framework that leverages large language models to automate definition extraction from academic literature. Focusing on the media bias domain, the…
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
Zhicheng Dou, Yuchen Guo, Ching-Chun Chang +2
The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. Whi…
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance
Tinghui Ouyang, AprilPyone MaungMaung, Koichi Konishi +2
In the era of large AI models, the complex architecture and vast parameters present substantial challenges for effective AI quality management (AIQM), e.g. large language model (LL…
Cross-Attention Watermarking of Large Language Models
Folco Bertini Baldassini, Huy H. Nguyen, Ching-Chung Chang +1
A new approach to linguistic watermarking of language models is presented in which information is imperceptibly inserted into the output text while preserving its readability and o…
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations
Hoang-Quoc Nguyen-Son, Seira Hidano, Kazuhide Fukushima +2
Adversarial attacks reveal serious flaws in deep learning models. More dangerously, these attacks preserve the original meaning and escape human recognition. Existing methods for d…