A development team has built a service that converts natural language questions into database queries. Currently, users must provide very specific, structured requests like: 'Generate a SQL query to SELECT the 'name' and 'email' columns FROM the 'users' table WHERE the 'signup_date' is after '2023-01-01'.' The team wants to enable users to make much simpler requests, such as 'show me all users who signed up this year'.

The team proposes a solution: they will create a large dataset of thousands of 'simple request' and corresponding 'highly-structured request' pairs. They will then use this dataset to conduct a specialized training process on their existing language model. After this process, they expect the model to correctly interpret simple requests without further guidance.

Critique the team's proposed solution. Is this approach likely to succeed? Justify your reasoning by explaining the underlying mechanism that allows the model's behavior to change. What is the primary trade-off the team is making by implementing this solution?