A company is developing a language model to analyze and find critical clauses within lengthy legal contracts, which often exceed 50,000 tokens. They are considering two architectural approaches to manage the computational cost of self-attention over these long sequences:

• Approach 1: A sparse attention mechanism where each token only attends to a small, fixed subset of other tokens (e.g., local neighbors and a few global tokens).
• Approach 2: A method that approximates the full attention matrix with a simpler, low-rank version to reduce computational complexity.

Evaluate the potential trade-offs of each approach for this specific task. Which approach would you recommend and why? Justify your reasoning by considering both computational efficiency and the model's potential performance on the task.