cs.AIJul 18, 2026

Similarity All The Way Up: Multilingual Generalization in LLMs Relies on Language-Level Similarity Structures

Authors: Supantho Rakshit, Adele Goldberg, Henry Conklin

Abstract

As Large Language Models (LLMs) grow more capable across diverse tasks, their (in)ability to generalize remains difficult to quantify and poorly understood beyond limited domains. In particular, LLMs are known to struggle generalizing multilingually, to languages outside of English, and that are poorly attested in their training data. To understand why this may be, and what enables some models to perform better than others, we turn to a long history of work across the cognitive sciences, arguing that successful generalization derives from appropriate representations in similarity space. We look at how well LLMs' representations capture the hierarchical similarity structure between distinct languages. Strikingly, we show LLMs' latent representations largely recover the hierarchical structure of the Indo-European language family tree -- grouping languages that are members of the same subfamily closely together in representation space. Furthermore, we show that the degree to which models reflect the similarity structure of languages correlates with their performance on XNLI, a multilingual natural language inference benchmark. This extends classic work on similarity-driven generalization at scale, showing how models that represent similar languages similarly generalize better from one language to another.

Explore similar work

CardsList
Jun 1, 2026cs.CL

Multilinguality of Large Language Models From a Structural Perspective

Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data, even though English dominates the training data. Prior work focusing on token representations has revealed how those LLMs process non-English text. Although these analyses have provided insightful findings, they fail to capture a structural view, which is an inherent property of language. In this study, we explore the multilinguality of LLMs through representational structural analysis. Our findings reveal that low-resource languages are structurally more different from English than high- and mid-resource languages, and that language-specific post-training alters their structures while preserving inter-language relationships.
Haruki Sakajo, Yusuke Sakai, Hidetaka Kamigaito +1
Multilingual Large Language ModelsMultilingualLow-Resource LanguagesRepresentational Capacity
AskPDF
Apr 14, 2026cs.CL

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

Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities across languages. We present a systematic, controlled study of the interplay between training language coverage, model scale, and task domain, based on 220 supervised fine-tuning runs on parallel translated multilingual data mixtures spanning mathematical reasoning and API calling tasks, with models up to 8B parameters. We find that English-only post-training is typically suboptimal: incorporating even a single non-English language improves both English performance and cross-lingual generalization. Increasing language diversity during post-training generally yields further gains, particularly for low-resource languages, while performance on high-resource languages tends to plateau rather than degrade. Moreover, greater language diversity enables strong zero-shot transfer to unseen languages, reducing the need for direct inclusion, though gains remain limited for typologically distant, low-resource languages.
Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin +2
Topic pending
AskPDF
May 27, 2026cs.CL

DEPART: DEcomposing PARiTy across Multilingual LLMs

Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biases unattributed and offering practitioners no actionable levers. We first establish that these gaps are systematic rather than artifacts of sampling noise via distribution-free Friedman and Kruskal--Wallis tests, then introduce a two-step Bayesian hierarchical framework that decomposes multilingual performance variance into interpretable components. First, isolating the variance attributable to language identity, we show that observable language features (script, family, typological distance) explain Rling2=79%R^2_{\text{ling}} = 79\% of this variance on understanding tasks and 92%92\% on reasoning, with a model's internal representational similarity to English emerging as the dominant predictor across both task buckets. Second, decomposing the full (model×\timesbenchmark×\timeslanguage) cube, we find that NLU and reasoning have fundamentally divergent variance profiles: model identity dominates understanding (66.7%66.7\% of variance), whereas the benchmark×\timesmodel interaction dominates reasoning (46.3%46.3\%). Together these results recast multilingual evaluation from passive performance mapping into an explainable, diagnostic framework with concrete levers for targeting the root drivers of language disparity.
Manan Uppadhyay, Prashant Kodali, Pranjal Chitale +3
Multilingual Large Language ModelsEnglishIdentity
AskPDF