Cover Image

Studies with impossible languages falsify LMs as models of human language

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bowers, Jeffrey S., Mitchell, Jeff
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Humans and transformer LMs: Abstraction drives language learning
by: Jian, Jasper, et al.
Published: (2026)

Language models as tools for investigating the distinction between possible and impossible natural languages
by: Kallini, Julie, et al.
Published: (2025)

Do large language models resemble humans in language use?
by: Cai, Zhenguang G., et al.
Published: (2023)

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

Multilingual large language models leak human stereotypes across language boundaries
by: Cao, Yang Trista, et al.
Published: (2023)

Aligning language models with human preferences
by: Korbak, Tomasz
Published: (2024)

Kallini et al. (2024) do not compare impossible languages with constituency-based ones
by: Hunter, Tim
Published: (2024)

Are they human? Detecting large language models by probing human memory constraints
by: Schug, Simon, et al.
Published: (2026)

Large language models have learned to use language
by: Lupyan, Gary
Published: (2025)

How important is language for human-like intelligence?
by: Lupyan, Gary, et al.
Published: (2025)

Strong and weak alignment of large language models with human values
by: Khamassi, Mehdi, et al.
Published: (2024)

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

A suite of LMs comprehend puzzle statements as well as humans
by: Goldberg, Adele E, et al.
Published: (2025)

Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models
by: Occhipinti, Daniela, et al.
Published: (2024)

Identifying and interpreting non-aligned human conceptual representations using language modeling
by: Bao, Wanqian, et al.
Published: (2024)

Retrieval augmentation of large language models for lay language generation
by: Guo, Yue, et al.
Published: (2022)

Can we teach language models to gloss endangered languages?
by: Ginn, Michael, et al.
Published: (2024)

Tiny language models
by: Gross, Ronit D., et al.
Published: (2025)

Comparing large language models and human programmers for generating programming code
by: Hou, Wenpin, et al.
Published: (2024)

Large language models are not about natural language
by: Bolhuis, Johan J., et al.
Published: (2025)

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

AI shares emotion with humans across languages and cultures
by: Wu, Xiuwen, et al.
Published: (2025)

Prompting language influences diagnostic reasoning and accuracy of large language models
by: Bazoge, Adrien, et al.
Published: (2026)

Why do language models perform worse for morphologically complex languages?
by: Arnett, Catherine, et al.
Published: (2024)

InstructPatentGPT: Training patent language models to follow instructions with human feedback
by: Lee, Jieh-Sheng
Published: (2024)

When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models
by: Amouyal, Samuel Joseph, et al.
Published: (2025)

A semantic embedding space based on large language models for modelling human beliefs
by: Lee, Byunghwee, et al.
Published: (2024)

Child vs. machine language learning: Can the logical structure of human language unleash LLMs?
by: Sauerland, Uli, et al.
Published: (2025)

Humans overrely on overconfident language models, across languages
by: Rathi, Neil, et al.
Published: (2025)

Dynamic data sampler for cross-language transfer learning in large language models
by: Li, Yudong, et al.
Published: (2024)

InkubaLM: A small language model for low-resource African languages
by: Tonja, Atnafu Lambebo, et al.
Published: (2024)

Effective vocabulary expanding of multilingual language models for extremely low-resource languages
by: Zheng, Jianyu
Published: (2026)

Large language models show fragile cognitive reasoning about human emotions
by: Bhattacharyya, Sree, et al.
Published: (2025)

Can language models learn analogical reasoning? Investigating training objectives and comparisons to human performance
by: Petersen, Molly R., et al.
Published: (2023)

Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
by: Timkey, William, et al.
Published: (2026)

ESG Sentiment Analysis: comparing human and language model performance including GPT
by: Derrick, Karim
Published: (2024)

Compositional preference models for aligning LMs
by: Go, Dongyoung, et al.
Published: (2023)

Post-training makes large language models less human-like
by: Binz, Marcel, et al.
Published: (2026)

A closer look at how large language models trust humans: patterns and biases
by: Lerman, Valeria, et al.
Published: (2025)

Demystifying optimized prompts in language models
by: Melamed, Rimon, et al.
Published: (2025)