Reproduce “Scaling and context steer LLMs along the same computational path as the human brain” on OpenResearch

About this paper

Joséphine Raugel, Stéphane d'Ascoli, Jérémy Rapin, Valentin Wyart, Jean-Rémi King

Researchers from Meta AI and LNC ENS-PSL demonstrated that large language models (LLMs) process language information in a temporal sequence mirroring human brain activity. This brain-like computational path consistently emerges and strengthens with increased LLM size and longer input context, indicating a shared processing dynamic.

Key insights

• The convergence between LLMs and the human brain extends beyond static representational similarities to encompass the dynamic, sequential nature of information processing.
• This brain-like computational path is a fundamental property of LLMs, emerging largely independently of specific architectural choices (e.g., transformers vs. state-space models), but dependent on sufficient scaling and context integration.
• LLMs that achieve better overall alignment with neural signals also tend to exhibit a more closely aligned sequential computational path, suggesting an intrinsic link between content capture and process alignment.

Summary by alphaXiv.