👉 Trigger engineering is a crucial aspect of model design in machine learning, particularly in sequence-to-sequence tasks like translation or text generation. It involves the strategic addition of specific elements, known as triggers, to input sequences to guide the model's attention and decision-making processes. These triggers can be words, phrases, or even learned representations that highlight important contextual information, helping the model focus on relevant parts of the input when generating outputs. By carefully designing these triggers, engineers can improve the model's performance, ensuring it captures and utilizes key contextual cues effectively, leading to more accurate and coherent outputs. This technique is especially vital in handling long-range dependencies and maintaining the integrity of complex sentence structures.
