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Focuses on identifying and classifying outputs generated by language models, including distinguishing between generated and human text. The research emphasizes the development of tools and techniques to detect nuances in language model outputs.
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 Identifying Bias in Machine-generated Text Detection Kevin Stowe Svetlana Afanaseva Rodolfo Raimundo Yitao Sun Kailash Patil | |||
 To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis Federico Bianchi Yongchan Kwon Zachary Izzo Linjun Zhang James Zou | |||
 DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution L. D. M. S. Sai Teja N. Siva Gopala Krishna Ufaq Khan Muhammad Haris Khan Partha Pakray Atul Mishra | |||
 The Erosion of LLM Signatures: Can We Still Distinguish Human and LLM-Generated Scientific Ideas After Iterative Paraphrasing? Sadat Shahriar Navid Ayoobi Arjun Mukherjee | |||
 Simplex-Optimized Hybrid Ensemble for Large Language Model Text Detection Under Generative Distribution Drif Sepyan Purnama Kristanto Lutfi Hakim Dianni Yusuf | |||
 A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Features Sergey K. Aityan William Claster Karthik Sai Emani Sohni Rais Thy Tran | |||
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