Pooling Attention: Evaluating Pretrained Transformer Embeddings for Deception Classification
Published:Nov 28, 2025 08:32
•1 min read
•ArXiv
Analysis
This article from ArXiv focuses on evaluating pretrained Transformer embeddings for deception classification. The core idea likely involves using techniques like pooling attention to extract relevant information from the embeddings and improve the accuracy of identifying deceptive content. The research likely explores different pooling strategies and compares the performance of various Transformer models on deception detection tasks.
Key Takeaways
Reference
“The article likely presents experimental results and analysis of different pooling methods applied to Transformer embeddings for deception detection.”