The study examines whether bilingual language models can reflect the influence of a reader’s first language (L1) on second-language (L2) reading. By comparing bilingual models trained on an L1 that matches the readers’ backgrounds to monolingual English models and bilingual models trained on other L1s, the authors find that matching L1 improves predictions of L2 reading times, especially in late-stage eye-tracking measures and among intermediate-proficiency readers. The effect is modulated by the overlap between L1 and L2 scripts, indicating that bilingual models capture cross-linguistic influence in L2 reading behavior.
By Abderrahmane Issam, Irene E. Winther, Stefan L. Frank
The study examines how reader proficiency influences the relationship between layer-wise surprisal from large language models (LLMs) and eye-tracking gaze measures. Using the MECO L2 corpus, researchers compared high- and low-proficiency readers on first-pass gaze duration (FPGD) and total gaze duration (TGD), finding that lower-proficiency readers exhibit deeper Predictive Depth for FPGD, while TGD shows deeper Predictive Depth across both groups. The results suggest that the distribution of predictive power across LLM layers relates to the timing and breadth of reading processes and varies with reader proficiency.
By Akio Hayakawa, Horacio Saggion
arXiv:2607. 08152v1 Announce Type: cross Abstract: On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56--63% AUROC, while gaze-only models operate at chance.
By Sumin Lee, Kyeonghun Kim, Subeen Lee, Jiwon Yang, Tien Nguyen, Ken Ying-Kai Liao, Nam-Joon Kim
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 investigates how Vision‑Language Models (VLMs) often report high confidence even after self‑correcting or arriving at wrong answers, a phenomenon the authors attribute to the verbalized confidence being largely independent of the model’s reasoning trajectory. By analyzing content variation, token masking, and hesitation markers, the authors demonstrate that confidence does not adequately reflect the actual reasoning process and that calibration training can sometimes worsen this disconnect. To address this blind spot, they introduce the Trajectory‑Grounding Score (TGS) in two forms—TGS‑self and TGS‑pair—and propose TGS‑Bench, a suite of 10 benchmarks that reveal divergences between conventional calibration metrics and trajectory‑grounded confidence.
By Jisoo Yang, Jaeho Han, Trung X. Pham, Junyeong Kim
arXiv:2607.08152v2 Announce Type: replace-cross
Abstract: Predicting comprehension from eye movements could support adaptive reading interfaces. We present LEXIC, a compact recurrent model that predi...
By Sumin Lee, Kyeonghun Kim, Subeen Lee, Jiwon Yang, Hyunsu Go, Eunseob Choi, Ken Ying-Kai Liao, Nam-Joon Kim
arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
By Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor
arXiv:2610.04898v2 Announce Type: replace-cross
Abstract: This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortes...
By Hongao Zhu, Muxiaoqiao Xu, Yikang Liu, Siyuan Song, Yuxia Wang, Byung-Doh Oh, Hai Hu
arXiv:2609.36214v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematic...
By Yusheng Zhou, Eleanor Lin, David Jurgens
The study introduces a latent‑process multinomial processing tree (MPT) model to analyze human reading and comprehension of garden‑path sentences across four reading paradigms (eye tracking, uni‑ and bidirectional self‑paced reading, Maze). The model separates the likelihood of an incorrect initial analysis, the cost of encountering an incompatible continuation, and the cost of syntactic reanalysis, yielding more realistic parameter estimates when inattentive trials are considered. Cross‑validation shows that this MPT model predicts human reading patterns and end‑of‑trial judgments better than a model relying solely on large‑language‑model (LLM) surprisal, and that incorporating surprisal as an additional predictor further improves fit.
By Dario Paape, Tal Linzen, Shravan Vasishth
arXiv:2606.26040v2 Announce Type: replace
Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...
By Yves Ferstler, Adam Podoxin, Ty Brassington, Ga\"elle Laperri\`ere, Roman Grundkiewicz, Marie-Jean Meurs, Maite Taboada, Marzena Karpinska
arXiv:2609.14207v1 Announce Type: new
Abstract: We propose to finetune vision-language models to generate more pragmatically optimal referring expressions by transforming observations of incremental...
By T\'ea Wright, Alane Suhr