arXiv:2210.06336
Synthetic Text Detection: Systemic Literature Review
원문 초록
Within the text analysis and processing fields, generated text attacks have been made easier to create than ever before. To combat these attacks open sourcing models and datasets have become a major trend to create automated detection algorithms in defense of authenticity. For this purpose, synthetic text detection has become an increasingly viable topic of research. This review is written for the purpose of creating a snapshot of the state of current literature and easing the barrier to entry for future authors. Towards that goal, we identified few research trends and challenges in this field.
함께 살펴볼 논문
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Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
The Devil is in Fine-tuning and Long-tailed Problems:A New Benchmark for Scene Text Detection
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning
Visual Text Processing: A Comprehensive Review and Unified Evaluation
SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting
Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
토론
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