Divergences between Language Models and Human Brains

2023/11/15 by Yuchen Zhou, Zhou, Yuchen, Emmy Liu +7 · 3 citations
Computer Science · Neuroscience · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Language and cultural evolution #Machine Learning (cs.LG) #Neurobiology of Language and Bilingualism #Neurons and Cognition (q-bio.NC) #Topic Modeling

paper · doi:10.48550/arxiv.2311.09308

openalex publication_date 2023/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

. We validate these findings with human behavioral experiments and hypothesize that the gap is due to insufficient representations of social/emotional and physical knowledge in LMs. Our results show that fine-tuning LMs on these domains can improve their alignment with human brain responses.

Citations

• Towards a neural basis of auditory sentence processing
• Transformers: State-of-the-Art Natural Language Processing
• The neural architecture of language: Integrative modeling converges on predictive processing
• Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
• Scaling Instruction-Finetuned Language Models
• Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
• Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data
• Scaling laws for language encoding models in fMRI
• Llama 2: Open Foundation and Fine-Tuned Chat Models

Cited by

• This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
• Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
• Unraveling the cognitive patterns of Large Language Models through module communities

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