Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA)

Context: Token replacement classification is crucial in natural language processing (NLP), enhancing applications such as text correction and data augmentation. Efficient and accurate models are essential for improving language model performance.

Abstract

Problem: Traditional approaches often require extensive computational resources and large labeled datasets, leading to inefficiencies and limitations in practical applications.

Approach: This study explores using ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) for token replacement classification. A synthetic dataset was generated, and the ELECTRA model was fine-tuned and evaluated to assess its performance in distinguishing between real and replaced tokens.

Results: The ELECTRA model demonstrated fluctuating training and stable validation accuracy around 0.50. The loss plots indicated a gradual decrease in training loss, but the validation loss showed limited improvement. The confusion matrix revealed a strong bias toward predicting one class, highlighting significant classification challenges.

Conclusions: The results indicate that while ELECTRA shows potential for token replacement classification…