The study examines how reading times differ between human-written texts and rewrites generated by large language models (LLMs). Using a RoBERTa-RT model trained on eye-tracking data, the authors compare predicted reading times for 496 paired articles and find that LLM rewrites are shorter overall but have higher predicted reading times per word. These differences correlate with linguistic changes such as longer, less frequent words, greater lexical diversity, and higher contextual predictability.
By Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
The paper introduces Coupled Usage–Sense Processes (CUSP), a method that models lexical semantic change by coupling contextual distributions through latent usage components and using Markov composition to link adjacent time periods. CUSP quantifies change magnitude and timing, separates variation into component movement and internal reorganization, and attributes changes to specific transported component pairs. The approach is validated on synthetic data, English and German corpora, and a large corpus of US court opinions, providing detailed, text‑grounded insights into how word meanings evolve over time.
By Haruka Ezoe, Ryohei Hisano
The paper investigates why AI-generated text has become harder to understand, focusing on psycholinguistic features linked to human processing difficulty such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text from diverse domains, the study finds that frontier and near-frontier language models employ these difficult features more frequently than paraphrases from round-trip translation and human writing, including more inanimate subjects with animate verbs, longer dependencies, stacked nouns, and reduced object relatives. The authors hypothesize that AI models prioritize information density for tasks like math, code, and reasoning, diverging from human communication goals, and demonstrate this by fine‑tuning a model with a length penalty, which reduces human‑like linguistic behavior.
By Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald
What do a language model's hidden states say about the organization of a single text? From one forward pass, without training, we score every token position on two properties.
arXiv:2610.32309v1 Announce Type: new
Abstract: Why can a task-preserving rewrite turn a correct answer into a failure while a larger internal change leaves the answer intact? We develop a direction-...
By Zhibo Hu, Chen Wang, Yanfeng Shu, Liming Dong, Hye-young Paik, Liming Zhu
The paper argues that language operates with two parameters: amplitude, which measures how often words co‑occur, and phase, a signed relational factor that determines how co‑activated meanings combine and can reverse a meaning’s contribution. Unlike amplitude, phase is not captured by standard word embeddings or transformer attention weights and is indexed to individuals and dyadic interactions. The authors propose six empirical predictions to test phase’s role and suggest that future language models should incorporate agent‑indexed, phase‑bearing semantic states.
arXiv:2603. 00270v3 Announce Type: replace-cross Abstract: Large language models can process millions of tokens, yet how they handle conflicting information within context remains poorly understood.
By Sourav Chattaraj, Kanak Raj
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
The study examines how varying the context window size of GPT‑2 affects its alignment with human reading-time data. A U‑shaped relationship is found: very short windows (<20 tokens) capture local memory limits, while larger windows (500–1,000 tokens) provide the best overall fit. Counterfactual experiments that disrupt cross‑sentential coreference chains show that these long‑range coreference relations are crucial for the improved fit of larger context windows.
By Kohei Kajikawa, Lin Ai, Tatsuki Kuribayashi, Ethan Gotlieb Wilcox
arXiv:2608.22452v1 Announce Type: new
Abstract: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it...
By Francisco Portillo L\'opez
STRUCTURALCOST is a self‑paced reading dataset comprising 475 participants and 40,800 observations that isolates the processing cost of long‑distance subject‑verb dependency resolution. The study replicates a known psycholinguistic finding at NLP scale: human reading times at the main verb increase with dependency length, driven by syntactic embedding beyond linear distance. Various language models—n‑gram, SSMs, and transformers—partially mirror this graded difficulty profile but consistently underestimate the integration cost humans incur, a gap that persists across architectures and model sizes.
By Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel, Christophe Pallier, Guillaume Wisniewski, Beno\^it Crabb\'e
The study investigates whether large language models retain traces of abandoned syntactic analyses after encountering garden‑path sentences. Across 24 models ranging from 124 M to 70 B parameters, the authors find that a residue of the initial, incorrect analysis influences later object attachment, while an adaptation toward the resolved structure scales with disambiguation error. Activation‑patching experiments show that the residue is carried by noun‑phrase states computed under the object analysis, and that overriding these states largely removes the effect.
By Xiang Ding