How does human feedback participate in training?

Human feedback participates in training Large Language Models (LLMs) primarily through a technique called Reinforcement Learning from Human Feedback (RLHF). It provides explicit, quality signals to guide the model towards producing outputs that better align with human values and preferences.

Humans rank or rate different model outputs for the same prompt, indicating which responses are more helpful, honest, harmless, or stylistically appropriate. This human preference data is used to train a separate "reward model" that learns to mimic these preferences. The main LLM is then fine-tuned using reinforcement learning algorithms (like Proximal Policy Optimization - PPO) that optimize its outputs based on the scores predicted by the reward model. This process requires significant effort to collect high-quality human preference datasets and careful design to avoid reward hacking. It enables models to learn complex, nuanced objectives that are difficult to define solely with traditional datasets and loss functions. The primary implementation steps involve 1) generating diverse model outputs, 2) collecting human preference judgments on these outputs, 3) training a reward model to predict these preferences, and 4) using the reward model to guide RL fine-tuning of the main LLM.

This approach bridges the gap between raw language modeling and desirable AI behavior, significantly enhancing the safety, usefulness, and alignment of deployed models like advanced chatbots and assistants. It allows the model to incorporate complex human judgments about quality that go beyond simple correctness, leading to more natural and valuable interactions.