ArticleScience advances2024
Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- Active use of latent tree-structured sentence representation in humans and large language models.Nature human behaviour · 2026Article
- Brain-inspired cognitive state modeling of complex English discourse reading: an LLM-parameterized hierarchical semi-Markov approach.Frontiers in neurorobotics · 2026Article
- Event segmentation applications in large language model enabled automated recall assessments.Communications psychology · 2025Article
- Increasing alignment of large language models with language processing in the human brain.Nature computational science · 2025Article
- Distinctive Human Dynamics of Semantic Uncertainty: Contextual Bias Accelerates Lexical Disambiguation.Behavioral sciences (Basel, Switzerland) · 2025Article
- Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models.NPJ science of learning · 2025Article
- Computational Sentence-Level Metrics of Reading Speed and Its Ramifications for Sentence Comprehension.Cognitive science · 2025Article
- Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.Microbial biotechnology · 2024Review
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4 authors.
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Abstract
Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.
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