About Google GLaM
Google’s Generalist Language Model (GLaM) is an innovative language model that aims to make in-context learning more efficient. It is a trillion-weight model that can be trained and served efficiently due to its use of sparsity, and it achieves competitive performance on multiple few-shot learning tasks.
Here are four key features of Google’s GLaM
- Efficient Scaling: GLaM is designed to scale efficiently in terms of computation and energy use. It achieves this through a mixture-of-experts model, which allows it to handle a wide range of tasks while using fewer resources than traditional models.
- High-Quality Dataset: GLaM is built on a high-quality 1.6 trillion token dataset. This dataset includes a wide range of language usage scenarios, which helps the model to be more versatile and accurate in its predictions.
- Mixture of Experts Model: GLaM uses a mixture of experts (MoE) model, which can be thought of as having different submodels (or experts) that are each specialized for different inputs. This allows the model to dynamically select the most appropriate experts to process the data, improving its performance and efficiency.
- Energy Efficiency: Despite using more computation during training, GLaM is more energy-efficient than other models thanks to its efficient software implementation and the use of TPUv4. This makes it a more sustainable choice for large-scale language modeling tasks.