Evaluating Inter-Task Generalization

A language model's ability to generalize to new tasks is evaluated using a set of 5 new instruction-input pairs. The model's performance on each pair is scored on a scale of 0 to 1, yielding the scores: [0.9, 0.8, 0.3, 0.2, 0.7]. According to the formal condition for inter-task generalization, which is defined as the average performance over the new set exceeding a threshold (ε), does this model demonstrate this capability if the threshold is set at ε = 0.6?

A model's capability to perform well across a variety of different tasks is formally assessed using the condition: $\frac{1}{|D|} \sum_{(c',z') \in D} P(c', z', y') > \epsilon$ In this expression, what is the most critical characteristic of the set of new instruction-input pairs, denoted by $D$, for a valid evaluation?