:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Casto, Colton, Ivanova, Anna, Fedorenko, Evelina, Kanwisher, Nancy
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2511.19757
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dissociating language and thought in large language models
by: Mahowald, Kyle, et al.
Published: (2023)

Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models
by: Kauf, Carina, et al.
Published: (2024)

Different types of syntactic agreement recruit the same units within large language models
by: Kryvosheieva, Daria, et al.
Published: (2025)

Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
by: Zhuang, Chengxu, et al.
Published: (2023)

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
by: Zhuang, Chengxu, et al.
Published: (2024)

Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
by: Saha, Shreya, et al.
Published: (2025)

Representational Curvature Modulates Behavioral Uncertainty in Large Language Models
by: King, Jack, et al.
Published: (2026)

Language models align with brain regions that represent concepts across modalities
by: Ryskina, Maria, et al.
Published: (2025)

Representing Speech Through Autoregressive Prediction of Cochlear Tokens
by: Tuckute, Greta, et al.
Published: (2025)

Testing AI on language comprehension tasks reveals insensitivity to underlying meaning
by: Dentella, Vittoria, et al.
Published: (2023)

Can large language models understand uncommon meanings of common words?
by: Wu, Jinyang, et al.
Published: (2024)

The time scale of redundancy between prosody and linguistic context
by: Regev, Tamar I., et al.
Published: (2025)

Toward best research practices in AI Psychology
by: Ivanova, Anna A.
Published: (2023)

CMMLU: Measuring massive multitask language understanding in Chinese
by: Li, Haonan, et al.
Published: (2023)

"What's my model inside of?": Exploring the role of environments for grounded natural language understanding
by: Tamari, Ronen
Published: (2024)

Enriching language models with graph-based context information to better understand textual data
by: Roethel, Albert, et al.
Published: (2023)

What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models
by: Verhoef, Tessa, et al.
Published: (2024)

Large Language Model probabilities cannot distinguish between possible and impossible language
by: Leivada, Evelina, et al.
Published: (2025)

GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models
by: Jassim, Serwan, et al.
Published: (2023)

Out-of-distribution generalisation in spoken language understanding
by: Porjazovski, Dejan, et al.
Published: (2024)

As easy as PIE: understanding when pruning causes language models to disagree
by: Tropeano, Pietro, et al.
Published: (2025)

\textsc{CantoNLU}: A benchmark for Cantonese natural language understanding
by: Min, Junghyun, et al.
Published: (2025)

Towards understanding evolution of science through language model series
by: Dong, Junjie, et al.
Published: (2024)

Logical forms complement probability in understanding language model (and human) performance
by: Wang, Yixuan, et al.
Published: (2025)

Lost without translation -- Can transformer (language models) understand mood states?
by: Shivaprakash, Prakrithi, et al.
Published: (2025)

What can large language models do for sustainable food?
by: Thomas, Anna T., et al.
Published: (2025)

What does the Knowledge Neuron Thesis Have to do with Knowledge?
by: Niu, Jingcheng, et al.
Published: (2024)

DocLLM: A layout-aware generative language model for multimodal document understanding
by: Wang, Dongsheng, et al.
Published: (2023)

Diagnosing our datasets: How does my language model learn clinical information?
by: Jia, Furong, et al.
Published: (2025)

Sparse components distinguish visual pathways & their alignment to neural networks
by: Marvi, Ammar I, et al.
Published: (2025)

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation
by: Guo, Yue, et al.
Published: (2025)

The production of meaning in the processing of natural language
by: Agostino, Christopher J., et al.
Published: (2026)

How does fine-tuning improve sensorimotor representations in large language models?
by: Wu, Minghua, et al.
Published: (2026)

Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models
by: Ivanova, Anna A., et al.
Published: (2024)

SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models
by: Nalbandyan, Grigor, et al.
Published: (2025)

Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference
by: Dentella, Vittoria, et al.
Published: (2024)

Domain adapted machine translation: What does catastrophic forgetting forget and why?
by: Saunders, Danielle, et al.
Published: (2024)

What does it take to get state of the art in simultaneous speech-to-speech translation?
by: Wilmet, Vincent, et al.
Published: (2024)

RBCorr: Response Bias Correction in Language Models
by: Bhatt, Om, et al.
Published: (2026)

Integrating large language models and active inference to understand eye movements in reading and dyslexia
by: Donnarumma, Francesco, et al.
Published: (2023)