AI Generated Text
AI-generated text detection focuses on distinguishing computer-generated text from human-written content, primarily to combat misinformation and academic dishonesty. Current research emphasizes developing robust detection methods, often employing deep learning architectures like transformers, and exploring techniques to improve the accuracy and generalizability of these methods across various languages and domains, including evaluating the effectiveness of different features like stylometry and semantic analysis. The ability to reliably detect AI-generated text is crucial for maintaining the integrity of scientific research, ensuring the authenticity of online information, and addressing ethical concerns surrounding the use of large language models.
Papers
Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
Xiaoman Xu, Xiangrun Li, Taihang Wang, Jianxiang Tian, Ye Jiang
RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts
Mohammad Heydari Rad, Farhan Farsi, Shayan Bali, Romina Etezadi, Mehrnoush Shamsfard
Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey
Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, Amrit Singh Bedi
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
Rongsheng Wang, Qi Li, Sihong Xie
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
Tharindu Kumarage, Paras Sheth, Raha Moraffah, Joshua Garland, Huan Liu
Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index
Megha Chakraborty, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Krish Sharma, Niyar R Barman, Chandan Gupta, Shreya Gautam, Tanay Kumar, Vinija Jain, Aman Chadha, Amit P. Sheth, Amitava Das