arXiv Computation and Language
Sep 30

Reader Proficiency Shapes Layer-wise Surprisal Profiles

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 Computation and Language
Sep 3

Do Cantonese-Adapted Language Models Better Predict Cantonese Reading? A Cross-Model Eye-Tracking Evaluation

The study investigates whether language models trained specifically on Cantonese better predict human reading patterns by comparing eye-tracking data with information-theoretic metrics derived from several models. Two adaptation contrasts were examined: a lightly adapted CKIP GPT-2 Tiny versus its Cantonese derivative JED351, and a heavily adapted Qwen2.5-7B versus CantoneseLLM-7B. Results show that the extensively Cantonese-trained CantoneseLLM-7B consistently outperforms others on lexical surprisal and joint metrics, while entropy reduction favors the less adapted CKIP model, indicating that deeper Cantonese-specific training can improve predictive alignment but that rankings vary by metric.

By Ziqi Zhang, Emmanuele Chersoni, Mohammad Momenian
arXiv AI
Jul 10

LEXIC: Lightweight Eye-tracking eXtension via Injected Complexity

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