Franken-Adapter: Cross-Lingual Adaptation of LLMs by Embedding Surgery

• URL: http://arxiv.org/abs/2502.08037v1
• Date: Wed, 12 Feb 2025 00:38:11 GMT
• Title: Franken-Adapter: Cross-Lingual Adaptation of LLMs by Embedding Surgery
• Authors: Fan Jiang, Honglin Yu, Grace Chung, Trevor Cohn,
• Abstract summary: We present $textitFranken-Adapter$, a modular language adaptation approach for decoder-only Large Language Models. Our method begins by creating customized vocabularies for target languages and performing language adaptation through embedding tuning on multilingual data. Experiments on $ttGemma2$ models with up to 27B parameters demonstrate improvements of up to 20% across 96 languages, spanning both discriminative and generative tasks.
• Score: 31.516243610548635
• License:
• Abstract: The capabilities of Large Language Models (LLMs) in low-resource languages lag far behind those in English, making their universal accessibility a significant challenge. To alleviate this, we present $\textit{Franken-Adapter}$, a modular language adaptation approach for decoder-only LLMs with embedding surgery. Our method begins by creating customized vocabularies for target languages and performing language adaptation through embedding tuning on multilingual data. These pre-trained embeddings are subsequently integrated with LLMs that have been instruction-tuned on English alignment data to enable zero-shot cross-lingual transfer. Our experiments on $\texttt{Gemma2}$ models with up to 27B parameters demonstrate improvements of up to 20% across 96 languages, spanning both discriminative and generative tasks, with minimal regressions ($<$1%) in English. Further in-depth analysis reveals the critical role of customizing tokenizers in enhancing language adaptation, while boosting inference efficiency. Additionally, we show the versatility of our method by achieving a 14% improvement over a math-optimized LLM across 20 languages, offering a modular solution to transfer reasoning abilities across languages post hoc.

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