Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·25d ago·5 min read
Objective vs. Search: Decomposing What Makes a Good Tokeniser
TL;DR
Two new tokenisation algorithms, BottomUpLL and TopDownComp, were developed to clarify the impact of optimisation objectives and search procedures on language model performance. Results indicate that the search procedure is more influential than the optimisation objective in achieving lower bits-per-byte.
✦ Why It Matters
Consider experimenting with bottom-up tokenisers to improve your language model's efficiency today.
Key Takeaways
- Explore BottomUpLL for better tokenisation efficiency.
- Evaluate the impact of search procedures on your models.
- Test different vocabulary sizes to optimize performance.
- Use findings to guide your tokeniser design choices.
How It Works
BottomUpLL employs a bottom-up merging strategy focused on maximizing log-likelihood, while TopDownComp uses a top-down pruning approach aimed at compression. This distinction allows for a clearer analysis of how each method influences language model performance.