When implementing trigger tokens in real-world applications, several challenges may arise. One challenge is designing effective trigger token sequences that accurately represent different translation directions while being flexible enough to adapt to various semantic nuances across languages. Another challenge is ensuring that the model learns from these triggers without becoming overly reliant on them, potentially hindering its ability to generalize well across diverse datasets and tasks. Additionally, managing the computational resources required for fine-tuning models with trigger tokens could pose a challenge in large-scale deployment scenarios.