Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 19·5 min read
Convergent Evolution: How Different Language Models Learn Similar Number Representations
TL;DR
Different language models, including Transformers and LSTMs, exhibit convergent evolution by learning similar number representations through distinct training signals. Key findings reveal that data, architecture, and training methods significantly influence the models' ability to classify numbers based on periodic features.
✦ Why It Matters
Consider adjusting your model architecture and training data to enhance number representation capabilities.
Key Takeaways
- Explore different model architectures for better number representation.
- Utilize multi-token addition problems in training datasets.
- Incorporate text-number co-occurrence signals in training.
- Analyze the impact of optimizers on feature learning.
How It Works
Models learn number representations through periodic features, with specific periods indicating how they classify numbers. The study shows that while all models exhibit periodicity, only some achieve geometric separability, which allows for effective classification based on these features.
This is influenced by the training data and the model's architecture.