Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies

1. 1Run successive self-training experiments on language models to observe changes in language structure
2. 2Analyze the output of self-trained models using metrics such as perplexity and syntax complexity
3. 3Compare the results across different models and training generations to identify patterns and trends
4. 4Apply insights from self-training experiments to improve language model architecture and training methods
5. 5Evaluate the impact of self-training on downstream NLP tasks, such as text classification and language translation

NLP researchers and language model developers can benefit from understanding the effects of self-training on language structure, to improve model performance and generate more diverse text

💡 Self-training language models leads to a restructuring of language, with surface markers becoming more prominent and deep syntax becoming less complex

🤖 Self-training language models doesn't flatten language, it restructures it! Surface markers amplify, while deep syntax dies 📉💻