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Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions
arXiv – CS AI|Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig||4 views
🤖AI Summary
New research analyzing 92 open-source language models reveals that factors beyond model size and training data significantly impact performance. The study shows that incorporating design features like data composition and architectural choices can improve performance prediction by 3-28% compared to using scale alone.
Key Takeaways
- →Smaller, well-designed models can outperform larger ones with more training tokens in certain cases.
- →Including design features beyond scale improves downstream performance prediction by 3-28%.
- →Optimal code-to-language ratio in training data appears to be 15-25% code content.
- →Rotary embeddings demonstrate superior performance compared to learned embeddings.
- →The framework enables more systematic investigation of how model development choices affect final capabilities.
#language-models#model-architecture#training-data#performance-optimization#open-source#embeddings#scaling-laws#model-design
Read Original →via arXiv – CS AI
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