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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Syntax vs. Semantics: How Transformers Learn Deep Dependencies
TL;DR
A new framework reveals how Transformers learn deep semantic dependencies through a competition between Surface Statistics and Deep Semantics, highlighting a 'Gradient Starvation' phenomenon. This understanding leads to a novel training objective that significantly improves performance on variable binding tasks, achieving over 2x better results than standard methods.
✦ Why It Matters
Implement the new topology-aligned contrastive objective to enhance your model's performance on semantic tasks today.
Key Takeaways
- Explore the impact of Surface Statistics on model training.
- Utilize Chain-of-Thought strategies for better reasoning.
- Apply the new contrastive objective for improved performance.
- Test on variable binding tasks to validate enhancements.
How It Works
The proposed framework models the learning of deep semantic dependencies as a competition between easily learned surface statistics and more complex deep semantics. During early optimization, the model experiences 'Gradient Starvation', where the error signals for sparse semantic dependencies are suppressed, leading to a sudden phase transition in learning.
By employing Chain-of-Thought strategies, which break down reasoning into explicit steps, the model can effectively bypass this suppression, allowing for better structural reasoning.