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.
Recent articles
- Notes on OpenAI's new o1 chain-of-thought models - 12th September 2024
- Notes from my appearance on the Software Misadventures Podcast - 10th September 2024
- Teresa T is name of the whale in Pillar Point Harbor near Half Moon Bay - 8th September 2024