The study investigates how disrupting conceptual versus referential information in short narratives affects human reading and large language model (LLM) processing. In humans, conceptual disruptions cause a strong, localized processing cost that peaks early and declines quickly, while referential disruptions produce weaker, gradually decreasing effects that are more influenced by sentence boundaries. In LLMs, both disruptions appear immediately at the manipulated word; surprisal patterns mirror human reading, whereas output-layer representations show that referential disruption initially causes a larger displacement before both types decay following a power-law.
By Rui He, Nihal Altay, Wolfram Hinzen
arXiv:2505.12196v2 Announce Type: replace
Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conject...
By Yi-Chien Lin, Hongao Zhu, William Schuler
Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model. I argue this claim is a tautology without further constraint: for any non-negative difficulty measure over units in context, there exists a language model whose surprisal is an affine function of it under mild technical conditions.
arXiv:2608. 14681v1 Announce Type: cross Abstract: Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior.
By Jinglei Ren, Yuyue Wang
arXiv:2606. 07555v1 Announce Type: cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways.
By Han-yu Wang
arXiv:2606. 11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content.
By Han-Jen Chang, Yasir \c{C}atal, Angelika Wolman, Agust\'in Ib\'a\~nez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff
The study investigates whether phonological traces of a first language persist in neural speech models after switching to a second language, mirroring phenomena observed in international adoptees. Using automatic speech recognition models trained on one language and then abruptly switched to another, researchers found that traces of the first language remained in the lowest, pre‑phonemic layers throughout second‑language training. These traces proved functional, as models with early exposure re‑learned their lost first language 14% faster than naive models, an advantage that vanished when the earliest layers were replaced with those from a non‑adopted model.
By Peter Plantinga, Charlotte Moore, Peter W. Donhauser, Krista Byers-Heinlein, Denise Klein
arXiv:2604. 19139v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics--repetitive, formulaic linguistic patterns that pervade model outputs.
By Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang, Ran Wang
arXiv:2608.30583v1 Announce Type: new
Abstract: Standard language proficiency tests rely on linguistic tasks such as vocabulary, grammar and reading comprehension quizzes. An alternative, cognitively...
By Shachar Frenkel, Ido Falah, Omer Shubi, Yevgeni Berzak
arXiv:2607. 13568v1 Announce Type: cross Abstract: Can a language model estimate its familiarity with an entity before generating an answer?
By Grzegorz Brzezinka
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
By Jelena Meyer, David Garcia, Dirk U. Wulff
arXiv:2605. 26795v2 Announce Type: replace Abstract: Chain-of-thought (CoT) prompting enhances large language model performance, yet what drives these gains remains unclear.
By Xiang Wang, Wei Wei