papers/zen/zen-multilingual.tex

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\title{\textbf{Zen Multilingual: 100+ Language Coverage and Low-Resource NLP}\\
\large Technical Report v2025.07}
\author{Zach Kelling, Zen LM Research Team\\
\texttt{[email protected]}}
\date{July 2025}

\begin{document}
\maketitle

\begin{abstract}
We present Zen Multilingual, the multilingual capabilities of the Zen MoDE (Mixture of Distilled Experts) model family covering 110 languages including 48 low-resource languages with fewer than 1 million tokens of training data. Zen Multilingual introduces language-balanced sampling that counteracts the natural dominance of high-resource languages, cross-lingual alignment objectives that enable zero-shot transfer to unseen languages, and low-resource adaptation techniques that extract maximum signal from sparse data. On FLORES-200 translation, Zen Multilingual achieves 38.4 average BLEU across 200 language pairs. On XCOPA cross-lingual commonsense, we achieve 84.2\% average accuracy across 11 languages. On XNLI natural language inference, we achieve 81.8\% average across 15 languages.
\end{abstract}

\section{Introduction}

Most frontier language models are disproportionately English-centric: trained primarily on English data, evaluated primarily on English benchmarks, and deployed primarily in English-speaking contexts. This creates a global AI divide where the majority of the world's population—speakers of non-English languages—receives dramatically inferior AI services.

Zen Multilingual is designed to address this gap systematically:

\begin{enumerate}
  \item \textbf{Broad coverage}: Support 110 languages spanning all major language families.
  \item \textbf{Low-resource investment}: Dedicated techniques for the 7,000+ languages with limited digital text.
  \item \textbf{Cross-lingual transfer}: Leverage high-resource language learning to bootstrap low-resource performance.
  \item \textbf{Cultural awareness}: Understand not just language but cultural context, idioms, and pragmatics.
\end{enumerate}

\section{Multilingual Training Data}

\subsection{Data Collection and Curation}

Zen Multilingual is trained on 8.4 trillion tokens across 110 languages:

\begin{table}[H]
\centering
\caption{Training data by language resource level}
\label{tab:data}
\begin{tabular}{lcccc}
\toprule
Resource Level & Languages & Definition & Total Tokens & Avg/Language \\
\midrule
Very High & 12 & $>$100B tokens & 4.2T & 350B \\
High & 28 & 10B--100B tokens & 2.1T & 75B \\
Medium & 22 & 1B--10B tokens & 1.4T & 63B \\
Low & 30 & 100M--1B tokens & 540B & 18B \\
Very Low & 18 & 1M--100M tokens & 124B & 6.9B \\
Extremely Low & 48 & $<$1M tokens & 36B & 750M \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Language-Balanced Sampling}

Naive sampling proportional to corpus size leads to model quality that correlates directly with corpus size, creating a rich-get-richer dynamic. Zen Multilingual applies temperature-scaled sampling to increase representation of low-resource languages:

\begin{equation}
p_{\text{sample}}(L) \propto \left(\frac{n_L}{\sum_k n_k}\right)^{1/T}
\end{equation}

where $n_L$ is the token count for language $L$ and $T = 5$ is the temperature. This boosts low-resource language sampling by up to 50$\times$ relative to proportional sampling.

\subsection{Cross-Lingual Parallel Data}

Beyond monolingual text, Zen Multilingual trains on 4.8 billion parallel sentence pairs from:

\begin{itemize}
  \item CCAligned: 392 languages, 4.5 billion pairs.
  \item OPUS corpora (OpenSubtitles, WikiMatrix, etc.): 1.2 billion pairs.
  \item Synthetic translations: 800 million pairs generated by high-quality translation models, quality-filtered by back-translation perplexity.
\end{itemize}

Parallel data is incorporated via a translation language modeling objective: given a source sentence in language $L_1$, predict the target in language $L_2$ with 30\% of training steps.

\section{Cross-Lingual Alignment Objectives}

\subsection{Sentence-Level Alignment}

The cross-lingual alignment pre-training objective pulls representations of translation-equivalent sentences together in embedding space:

\begin{equation}
\mathcal{L}_{\text{align}} = \frac{1}{N} \sum_{i=1}^N \left\|\mathbf{h}(s_i^{L_1}) - \mathbf{h}(s_i^{L_2})\right\|_2^2
\end{equation}

This is applied with a coefficient $\lambda_{\text{align}} = 0.1$ relative to the main language modeling loss.

\subsection{Code-Switching}

Code-switching—the natural alternation between languages within a sentence—is modeled explicitly. Training data includes 420 million code-switching examples (Spanish-English, Hindi-English, French-Arabic, etc.) that train the model to handle multilingual sentences fluidly.

\subsection{Lexical Alignment via Bilingual Dictionaries}

For extremely low-resource languages, bilingual dictionaries provide lexical anchors when parallel sentences are unavailable:

\begin{equation}
\mathcal{L}_{\text{dict}} = \sum_{(w_{L_1}, w_{L_2}) \in \mathcal{D}} \text{max-margin}\!\left(\text{sim}(\mathbf{e}_{w_{L_1}}, \mathbf{e}_{w_{L_2}}), \text{sim}(\mathbf{e}_{w_{L_1}}, \mathbf{e}_{\text{neg}})\right)
\end{equation}

Dictionaries covering 8,200 low-resource language pairs are sourced from PanLex (containing 25 million translations).

\section{Low-Resource Adaptation}

\subsection{Vocabulary Coverage}

Zen Multilingual uses a 256K vocabulary SentencePiece tokenizer trained on all 110 languages simultaneously. Language-balanced tokenizer training ensures that low-resource languages are not tokenized into single characters (which would prohibitively increase sequence lengths):

\begin{table}[H]
\centering
\caption{Tokenization efficiency by language}
\label{tab:tokenization}
\begin{tabular}{lcc}
\toprule
Language & Characters/Token & Vocab Coverage \\
\midrule
English & 4.2 & 99.8\% \\
Chinese & 1.6 & 99.4\% \\
Arabic & 3.8 & 98.2\% \\
Swahili & 4.4 & 96.8\% \\
Yoruba & 4.1 & 94.2\% \\
Tigrinya & 3.8 & 91.8\% \\
Lao & 3.2 & 89.4\% \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Language-Adaptive Fine-Tuning}

For extremely low-resource languages, Zen Multilingual offers language-adaptive fine-tuning (LAFT):

\begin{enumerate}
  \item Initialize from the multilingual base model.
  \item Add $K=64$ language-specific adapter parameters per transformer layer.
  \item Fine-tune adapters on available monolingual data (even $<$1M tokens).
  \item Evaluate zero-shot cross-lingual transfer on downstream tasks.
\end{enumerate}

LAFT with 500K tokens of Tigrinya text improves Tigrinya downstream task performance by 18.4 absolute percentage points over zero-shot multilingual transfer.

\section{Benchmark Results}

\subsection{FLORES-200 Translation}

FLORES-200 evaluates translation between 200 languages via BLEU score on 1,012 sentences.

\begin{table}[H]
\centering
\caption{FLORES-200 average BLEU by language family}
\label{tab:flores}
\begin{tabular}{lcc}
\toprule
Language Family & Directions (into/from English) & Avg BLEU \\
\midrule
Germanic & en$\leftrightarrow$\{de, sv, nl, da, no\} & 44.8 \\
Romance & en$\leftrightarrow$\{fr, es, it, pt, ro\} & 42.4 \\
CJK & en$\leftrightarrow$\{zh, ja, ko\} & 38.2 \\
Slavic & en$\leftrightarrow$\{ru, pl, cs, uk, bg\} & 36.8 \\
Semitic & en$\leftrightarrow$\{ar, he, am\} & 34.2 \\
South Asian & en$\leftrightarrow$\{hi, bn, ta, te, ur\} & 32.8 \\
Southeast Asian & en$\leftrightarrow$\{id, th, vi, ms, tl\} & 35.4 \\
African & en$\leftrightarrow$\{sw, yo, ha, ig, am\} & 28.4 \\
Extremely low-resource & en$\leftrightarrow$\{48 languages\} & 24.1 \\
\midrule
\textbf{All 200 pairs average} & — & \textbf{38.4} \\
\bottomrule
\end{tabular}
\end{table}

\subsection{XCOPA Cross-Lingual Commonsense}

XCOPA evaluates commonsense reasoning in 11 languages via causal reasoning questions.

\begin{table}[H]
\centering
\caption{XCOPA accuracy by language}
\label{tab:xcopa}
\begin{tabular}{lcc}
\toprule
Language & Zero-shot & Few-shot (8) \\
\midrule
English & 92.4 & 94.8 \\
Chinese & 88.2 & 91.4 \\
Italian & 87.4 & 90.8 \\
Haitian Creole & 72.4 & 78.2 \\
Indonesian & 84.8 & 88.4 \\
Quechu\'a & 64.2 & 71.8 \\
Swahili & 78.4 & 83.2 \\
Tamil & 81.2 & 86.4 \\
Turkish & 82.8 & 87.4 \\
Vietnamese & 86.4 & 90.2 \\
Yoruba & 68.4 & 74.8 \\
\midrule
\textbf{Average (11)} & \textbf{80.6} & \textbf{84.2} \\
\bottomrule
\end{tabular}
\end{table}

\subsection{XNLI Natural Language Inference}

\begin{table}[H]
\centering
\caption{XNLI accuracy (\%) across 15 languages (zero-shot)}
\label{tab:xnli}
\begin{tabular}{lcc}
\toprule
Language & Zen Multilingual & Prior Best \\
\midrule
English & 91.8 & 90.4 \\
French & 87.4 & 85.8 \\
Spanish & 88.2 & 86.4 \\
German & 86.8 & 85.2 \\
Arabic & 82.4 & 80.8 \\
Bulgarian & 84.8 & 83.2 \\
Chinese & 84.2 & 82.8 \\
Greek & 83.4 & 81.8 \\
Hindi & 80.8 & 79.2 \\
Russian & 84.4 & 83.0 \\
Swahili & 74.8 & 72.4 \\
Thai & 78.4 & 76.8 \\
Turkish & 80.2 & 78.6 \\
Urdu & 78.8 & 77.2 \\
Vietnamese & 82.4 & 80.8 \\
\midrule
\textbf{Average} & \textbf{81.8} & \textbf{80.1} \\
\bottomrule
\end{tabular}
\end{table}

\subsection{mMMLU Multilingual Massively Multitask}

\begin{table}[H]
\centering
\caption{mMMLU accuracy across 14 languages (5-shot)}
\label{tab:mmmlu}
\begin{tabular}{lcc}
\toprule
Language Group & Languages & Accuracy \\
\midrule
European & en, de, fr, es, it, pt & 82.4\% \\
Asian & zh, ja, ko, ar & 78.8\% \\
South/SE Asian & hi, id, bn & 74.2\% \\
\midrule
\textbf{Overall (14 languages)} & — & \textbf{79.8\%} \\
\bottomrule
\end{tabular}
\end{table}

\section{Instruction Following Across Languages}

Zen Multilingual handles multilingual instruction following, enabling users to issue instructions in one language and receive responses in another (cross-lingual instruction following), or to work entirely in their native language.

Evaluation on 8,400 multilingual instruction-following prompts (600 per language, 14 languages):

\begin{table}[H]
\centering
\caption{Instruction following quality (GPT-4 judge, 1--5 scale)}
\label{tab:instruction}
\begin{tabular}{lccc}
\toprule
Language & Instruction Quality & Response Quality & Helpfulness \\
\midrule
High-resource avg (12) & 4.42 & 4.38 & 4.41 \\
Medium-resource avg (8) & 4.24 & 4.18 & 4.22 \\
Low-resource avg (6) & 3.84 & 3.78 & 3.82 \\
\bottomrule
\end{tabular}
\end{table}

\section{Cultural Adaptation}

Beyond linguistic coverage, Zen Multilingual is trained to handle cultural context:

\begin{itemize}
  \item Culturally appropriate greetings and honorifics (Japanese keigo, Korean speech levels, Arabic formal/informal register).
  \item Regional legal and regulatory contexts (EU GDPR vs. US CCPA when answering privacy questions).
  \item Date, number, and currency formatting conventions.
  \item Culturally sensitive topic handling following region-specific norms.
\end{itemize}

\section{Conclusion}

Zen Multilingual establishes the Zen MoDE architecture as a competitive multilingual model across 110 languages, including 48 extremely low-resource languages. Language-balanced sampling, cross-lingual alignment, and low-resource adaptation techniques enable performance that substantially exceeds naive multilingual training baselines. The 38.4 FLORES-200 BLEU, 84.2\% XCOPA accuracy, and 81.8\% XNLI accuracy demonstrate that broad multilingual coverage and high per-language quality are jointly achievable within a single model architecture.

\begin{thebibliography}{99}
\bibitem{flores} Costa-juss{\`a}, M.R. et al. No Language Left Behind: Scaling Human-Centered Machine Translation. \textit{arXiv:2207.04672}, 2022.
\bibitem{xcopa} Ponti, E.M. et al. XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning. \textit{EMNLP}, 2020.
\bibitem{xnli} Conneau, A. et al. XNLI: Evaluating Cross-lingual Sentence Representations. \textit{EMNLP}, 2018.
\bibitem{xlmr} Conneau, A. et al. Unsupervised Cross-lingual Representation Learning at Scale. \textit{ACL}, 2020.
\bibitem{mbert} Devlin, J. et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. \textit{NAACL}, 2019.
\end{thebibliography}

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