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
The study investigates whether monolingual language models, trained without joint multilingual objectives, develop cross-lingual alignment. By evaluating models such as Goldfish and independently built monolingual systems, the authors find that alignable representational geometry emerges across layers, strengthening with larger data, larger models, or closer linguistic proximity. A single Procrustes rotation on parallel sentences can map hidden states between models, and applying this rotation to a German model’s residuals swaps factual predictions to those of the donor English model, demonstrating functional transfer.
By Ej Zhou, Suchir Salhan, Catherine Arnett, Anna Korhonen
We introduce Dango, a 1. 8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition (SLA).
The study evaluates how different input representations—orthographic text, IPA transcription, and romanization—affect cross‑lingual transfer in autoregressive multilingual language models. Across three model sizes and eight languages grouped into typologically motivated pairs, romanized pretraining consistently outperforms native orthography and IPA, especially as model scale increases. Fine‑tuning a text‑pretrained model on romanized data can harm performance on languages already covered by the base model, suggesting romanization should be integrated at pretraining rather than applied later.
By Muge Zhang, Aaron Jencks, Krishna Badikela, Yulia Tsvetkov, Sachin Kumar
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
By Dries Rooryck, Alex Cai, Yonatan Belinkov, David Alvarez-Melis, Kiant\'e Brantley
The paper investigates weight‑space merging of independently fine‑tuned multilingual machine translation models. Experiments show that merging is more successful when models share a target language, yet it still cannot match the peak performance of language‑specific checkpoints. When target languages differ, performance drops sharply, and analysis reveals that overlapping neuron activation and incompatible upper‑layer geometries cause these failures.
By Baban Gain, Trilok Nath Singh, Asif Ekbal
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
The paper investigates why large language models (LLMs) fail to transfer knowledge across languages. By pretraining 360M- and 7B-parameter models, the authors show that poor cross‑lingual generalization arises during pretraining and persists despite standard fixes. Using a controlled bilingual setup with identical text but disjoint token spaces, they demonstrate that token disjointness alone causes knowledge compartmentalization, and that mapping languages into a shared token space via word‑wise translation markedly improves cross‑lingual performance, recovering up to 12.6% of native‑language learning efficiency.
By Adam Gaber, Uriel Dolev, Elisabeth Fittschen, Bobby Cheng, Yuval Marton, Leshem Choshen
The paper investigates how multilingual large language models can be guided to reason more reliably in low- to mid-resource languages by selecting appropriate language modes during inference. Experiments with LLaMA and Qwen models show that using English context can correct errors from non‑English comprehension, but adding redundant bilingual context can cause interference. To balance this trade‑off, the authors propose Reliability‑Aware Adaptive Inference (RAAI), a training‑free test‑time framework that routes prompts based on Expected Calibration Error and gates reasoning with a mid‑layer Risk Index, achieving up to 37.7% accuracy gains and reduced calibration error on low‑resource languages.
By Ekata Mitra, Ameeta Agrawal
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
By Daniela Baiamonte, Elena Fano, Matteo Gabburo, Stefano Simonazzi, Leonardo Rigutini, Andrea Zugarini
arXiv:2604.13286v2 Announce Type: replace
Abstract: Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing...
By Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin, Dezhi Hong, Thomas Butler
arXiv:2609.01356v1 Announce Type: new
Abstract: Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they tra...
By Mikhail Sonkin, Tanja Baeumel, Daniil Gurgurov, Josef van Genabith, Simon Ostermann