The Two Tricks Hiding Inside Every Modern Language Model
📰 Medium · AI
Discover two key tricks behind modern language models, including removing position embeddings and using meaningless tokens, to improve their performance
Action Steps
- Remove position embeddings from a language model to reduce spatial dependencies
- Introduce meaningless tokens to a model's training data to improve its robustness
- Configure a language model to use a combination of both techniques for optimal results
- Test the performance of a language model with and without these tricks to compare their impact
- Apply these techniques to a real-world NLP task, such as text classification or language translation
Who Needs to Know This
NLP engineers and AI researchers can benefit from understanding these techniques to optimize their language models, while data scientists can apply these insights to improve their ML pipelines
Key Insight
💡 Removing position embeddings and using meaningless tokens can significantly improve the performance of modern language models
Share This
🤖 Did you know that modern language models rely on two surprising tricks? Removing position embeddings and using meaningless tokens can boost performance! 💡
Key Takeaways
Discover two key tricks behind modern language models, including removing position embeddings and using meaningless tokens, to improve their performance
Full Article
One removes position embeddings entirely. The other makes models stare at meaningless tokens on purpose. Both are load-bearing. Continue reading on Medium »
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