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EntGPT: Linking Generative Large Language Models with Knowledge Bases
Overall, the prompting method improves the micro-F_1 score of the original vanilla models by a large margin, on some cases up to 36% and higher, and obtains comparable performance across 10 datasets when compared to existing methods with SFT.
A Simple yet Efficient Ensemble Approach for AI-generated Text Detection
Experiments conducted on four benchmark datasets for generative text classification show performance improvements in the range of 0. 5 to 100\% compared to previous state-of-the-art approaches.
A Closer Look at Bearing Fault Classification Approaches
Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations.
Generative AI Text Classification using Ensemble LLM Approaches
For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0. 733 and 0. 649) for English and Spanish texts, respectively.
Anomaly Detection via Federated Learning
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior.
