Understanding large language models requires distinguishing between human projection and machine cognition.
Researchers believe that borrowing metaphors from fields such as physics, neuroscience, and psychology to understand LLMs can reveal certain aspects, but it also leads to anthropomorphic bias. They propose machine empiricism, emphasizing that LLMs build their own understanding based on the textual world, rather than imitating humans.