This process involves employing a specialized mechanism to deliberately constrain the operational scope of artificial intelligence models during their training phase. For instance, an AI designed to generate text might have its exposure limited to a specific vocabulary or stylistic framework. This contrasts with unrestricted training scenarios where the AI has free rein over a vast dataset.
Controlled exposure during training promotes efficiency and safety. By focusing the AI’s learning on a defined area, resources are used more effectively. It helps prevent the AI from developing undesirable outputs or behaviors, especially crucial in applications demanding precision and reliability. Historically, this approach has proven effective in mitigating potential risks associated with large language models and complex AI systems.
