arxiv preprint - Instruction-tuning Aligns LLMs to the Human Brain

In this episode we discuss Instruction-tuning Aligns LLMs to the Human Brain by Khai Loong Aw, Syrielle Montariol, Badr AlKhamissi, Martin Schrimpf, Antoine Bosselut. The paper examines whether instruction-tuning, a method for fine-tuning large language models (LLMs), makes their processing more human-like through two metrics: brain alignment and behavioral alignment. Results indicate instruction-tuning increases brain alignment with human neural activity by 6% on average but does not significantly impact behavioral alignment. A strong correlation is found between brain alignment and both the size of the model and its performance on tasks requiring world knowledge, suggesting that as LLMs better encode world knowledge, their internal representations align more closely with human brain activity.