A machine learning team is preparing to fine-tune a single pre-trained language model to perform several different tasks, ranging from simple sentiment classification to complex, multi-step reasoning problems. They are debating between two primary strategies for formatting the textual instructions in their training data:

1. Using a concise, unique task name as a prefix for each input (e.g., classify_sentiment:, solve_logic_puzzle:).
2. Providing a detailed, natural language description of the task for each input (e.g., Please analyze the sentiment of the following text and label it as positive, negative, or neutral. Text:).

Analyze the trade-offs between these two approaches. In your analysis, discuss the potential advantages and disadvantages of each method, considering factors like model performance on different task types, data preparation effort, and the model's ability to generalize to new, unseen instructions.