The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.
— PaLM 2 Technical Report, PDF
Recent articles
- LLM 0.22, the annotated release notes - 17th February 2025
- Run LLMs on macOS using llm-mlx and Apple's MLX framework - 15th February 2025
- URL-addressable Pyodide Python environments - 13th February 2025