A research team is building a language model for a niche, low-resource task: analyzing 18th-century legal documents. The dataset is small and the team has a very limited computational budget for training. They are considering two approaches for incorporating relative word positions into the model's attention mechanism.

• Approach A: Learn the positional biases as part of the model's parameters directly from the small dataset.
• Approach B: Use a pre-defined, fixed set of positional biases based on a general rule about word distance, which does not require learning additional parameters.

Which approach would you recommend for this project? Justify your decision by evaluating the primary trade-off between these two approaches in the context of the project's specific constraints.