arXiv AI September 2, 2026 By Saman Rahbar

The Curse of Multilinguality in Lexical Normalization

The paper investigates how many languages should be jointly trained in a single lexical normalization model. Using a fixed-capacity character-level model across twelve languages, it finds that accuracy peaks when a language is trained with only a few others—typically one to four—and then declines sharply as more languages are added, dropping about forty percent. A control experiment keeping total training data constant shows the decline is due to competition for model capacity rather than data scarcity, and no reliable typological rule predicts the optimal number of co‑training languages.

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benchmarks

arXiv Computation and Language

Aug 27

One Form to Transfer Them All: Pretraining Multilingual Language Models Beyond Native Orthography

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

llms fine-tuning

arXiv Machine Learning

Jul 28

No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study

arXiv:2410. 07809v2 Announce Type: replace-cross Abstract: Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost.

By G\"urkan Soykan, G\"ozde G\"ul \c{S}ahin

fine-tuning benchmarks

arXiv Computation and Language

Sep 22

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

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

llms fine-tuning

arXiv Machine Learning

Aug 11

Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation

arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.

By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli

llms rag nlp fine-tuning

arXiv AI

Sep 4

One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging

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

llms nlp fine-tuning

Hugging Face Trending Papers

Jul 23

DONDO: Open w2v-BERT Speech-Recognition Base Models for African Languages

We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2. 0 self-supervised speech encoder.

fine-tuning multimodal