ArticleAdvances in neural information processing systems2024
Divergences between Language Models and Human Brains.
Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Biomimetic Visual Sensing Framework: Unsupervised Orientation Topographic Mapping via Self-Organizing Neural Networks.Biomimetics (Basel, Switzerland) · 2026Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
- A Gaussian Mixture Model-Based Unsupervised Dendritic Artificial Visual System for Motion Direction Detection.Biomimetics (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using an LLM-based data-driven approach, we identify two domains that LMs do not capture well:
Identifiers
40433349PMC12108097What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.