Novel Approach to Temporal Drift Detection in Transformer Sentiment Models

This ArXiv paper investigates temporal drift detection within Transformer models, a crucial aspect of maintaining model accuracy over time. The focus on zero-training methods for social media data is particularly interesting and relevant.

• •Explores methods for detecting temporal drift in sentiment analysis models.
• •Focuses on a zero-training approach, potentially reducing training costs and improving adaptability.
• •The analysis is performed on real-world social media data, indicating practical relevance.