Domain
Artificial Intelligence, Machine Learning
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities.When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre-and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.
PaLM 2 is a state-of-the-art language model developed by Google, improving upon its predecessor PaLM with enhanced multilingual capabilities, reasoning abilities, and efficient compute usage. This paper discusses its architecture based on the Transformer, training strategies involving diverse multilingual datasets, and performance evaluations across various tasks, including natural language understanding, generation, and reasoning. PaLM 2 shows notable advancements in quality across various model sizes and in real-world applications, as well as in responsible AI evaluations, dealing with toxicity and biases effectively. The authors outline their scaling laws, dataset improvements, and the rigorous evaluation process undertaken to validate the model's capabilities. They also address potential biases in the training data and the ethical implications of deploying such models in various applications.
This paper employs the following methods:
- PaLM 2
- PaLM 2-S
- PaLM 2-M
- PaLM 2-L
The following datasets were used in this research:
- SQuAD
- RACE
- TyDi QA
- BIG-Bench
- BLEURT
- MQM
- ROUGE-2
- Accuracy
- F1-score
- Improved multilingual capabilities
- Enhanced reasoning performance
- Efficient computation for broader deployment
The authors identified the following limitations:
- Potential biases in training data
- Expected differences in user-facing products' performance
- Challenges in generalizing results across different contexts
- Number of GPUs: None specified
- GPU Type: None specified
Large Language Models
Multilinguality
Evaluation
Bias
Safety