LLM RLHF: The Secret Weapon of Vertex AI

Large neural network models known as “foundation models” are capable of producing text, images, speech, code, and other types of high-quality output

Organizations must adjust foundation models to behave and respond appropriately in order for them to best meet particular needs.

RLHF uses human feedback in the context of enterprise use cases to assist the model in producing outputs that satisfy particular requirements

Comparisons are used to gather data for reward modeling. To generate multiple responses, Google first feed the same prompt into one or more LLMs

The scores from the reward model must, to the greatest extent feasible, match the ranking

Google select a prompt from the dataset, generate a response using the LLM, and evaluate the response’s quality using the reward model

Customers of Vertex AI can tune PaLM 2, FLAN-T5, and Llama 2 models with RLHF by utilizing a Vertex AI Pipeline, which encapsulates the RLHF algorithm