GLaM is a language model from Google Research Brain Team that allows for more efficient in-context learning with large language models such as GPT-3. It enables users to perform few-shot learning with very few or no training examples, and can be trained and used more efficiently.
Benefits of GLaM include:
- More efficient scaling of language models
- Few-shot learning capabilities
- Ability to use very few or no training examples
- Reduced computational intensity for training and serving large models
Possible use cases for GLaM include leveraging the power of GPT text generation to create natural language conversations, enabling automated customer service, or providing more efficient machine translation services.
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