arXiv Computation and Language By Abderrahmane Issam, Irene E. Winther, Stefan L. Frank

Bilingual Language Models Capture Cross-Linguistic Influence in Second-Language Reading

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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
Aug 28

Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

The study compares an English-only and a bilingual decoder-only model, each 310 M parameters, trained on eight diverse languages while controlling for English exposure, compute, and document overlap. After aligning on shared English vocabulary, the authors find that token embeddings appear similar, but the deeper hidden states used for prediction diverge across models. This hidden‑state mismatch grows through middle transformer layers and persists despite controls, indicating that contextual processing differs between the models. "whyItMatters":"The findings show that embedding alignment can conceal significant internal representation differences, which is crucial for any downstream work that assumes aligned multilingual models are interchangeable."

By Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
arXiv Computation and Language
Sep 30

Cross-Linguistic Effects in Bilingual Phoneme BabyLMs

arXiv:2609.37121v1 Announce Type: new Abstract: Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by en...

By Nikitas Theodoropoulos, Maria Lymperaiou, Giorgos Filandrianos