Summary
Pre-training a large language model on extensive data serves as a potent starting point, establishing a foundational model with billions of parameters that possess an intuitive grasp of language. RLHF leverages human guidance to aid language models in acquiring favorable behaviors for designated tasks through fine-tuning generated outputs. This approach facilitates smaller models in achieving enhanced performance levels.
Pre-training a large language model
Pre-training a large language model on a vast corpus of data provides a powerful initialization, creating a foundational model with billions of parameters that have a subjective understanding of the language.
Performance for specific tasks
RLHF uses human guidance to help language models learn desirable behaviors for specific tasks through generated outputs fine-tuning, enabling smaller models to achieve improved performance.
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