It depends on the specific goals and objectives of the tests being designed. If the focus is on evaluating a model's ability to understand and generate human language, then training data from both philosophers and cognitive scientists could be useful.

For example, philosophers often write about abstract concepts and theories related to language, meaning, and communication, which could be useful for testing a model's ability to understand complex ideas and arguments. On the other hand, cognitive scientists often study the mental processes involved in language comprehension and production, which could provide valuable information for testing a model's ability to perform specific language tasks, such as recognizing linguistic patterns or generating coherent text.

In summary, both philosophical and scientific data could be useful for designing tests for Natural Language Models or AI, but the most relevant data would depend on the specific goals and objectives of the tests.
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