Learning from human feedback
One primary method of refining LLMs involves human operators who review model outputs and correct errors, which can range from grammatical mistakes to factual inaccuracies or contextually inappropriate responses. These corrections are then reincorporated into the training data, allowing the model to learn and improve from its mistakes. This continuous cycle of feedback and learning is vital for the evolution of the model’s accuracy and reliability.
In more sophisticated training setups, LLMs are configured to adapt based on feedback that is either positive or negative toward specific outputs. For instance, a response that receives positive feedback from a human reviewer might be reinforced, encouraging the model to produce similar responses in future interactions. On the other hand, responses that are poorly received can lead to negative reinforcement, which teaches the model to avoid producing such outputs in the future.
Another aspect of...
