Unfortunately I am not aware of any articles that can show convincing data that "LLMs don't learn like humans". I'm not really even sure what that means precisely.
Your statement could be understood to claim that language models cannot predict brain activations or vice versa? Predictive power is the foundation of science, so it's one of the better tests we have for these types of problems. To that point, there is substantial evidence of similarity and predictive power, as noted below.
Perhaps you mean something else? Surely you're point is not that GPUs simply aren't human. Of course - there are differences at various levels. So at some level, everyone recognizes they're not identical. Perhaps it's that attention architectures are not reproduced within neural microarchitectures in tbe neocortex? I haven't seen any studies on this within the relevant cortical areas to address this, though microarchitecture matching may not be required for output distribution matching. The interesting endeavour for science to show right now is how these systems are similar to our processing mechanisms. The ways in which they're not similar typically tend to be uninteresting, or obvious.
The fact that we can predict brain activity from NN model activations raises many more opportunities in science, and the predictive power has gotten far better with these advances,.
Here are a small set of articles from MIT, deepmind, etc. for some of the points I made:
- Tuckute, Greta, Aalok Sathe, Shashank Srikant, Maya Taliaferro, Mingye Wang, Martin Schrimpf, Kendrick Kay, and Evelina Fedorenko. "Driving and suppressing the human language network using large language models." Nature Human Behaviour (2024): 1-18.
- Schrimpf, Martin, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar et al. "Brain-score: Which artificial neural network for object recognition is most brain-like?." BioRxiv (2018): 407007.
- Pereira, Francisco, Bin Lou, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Matthew Botvinick, and Evelina Fedorenko. "Toward a universal decoder of linguistic meaning from brain activation." Nature communications 9, no. 1 (2018): 963.
- Arend, Luke, Yena Han, Martin Schrimpf, Pouya Bashivan, Kohitij Kar, Tomaso Poggio, James J. DiCarlo, and Xavier Boix. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. Center for Brains, Minds and Machines (CBMM), 2018.