Languages are Modalities: Cross-Lingual Alignment via Encoder Injection
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| Format: | Preprint |
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2025
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| author | Agarwal, Rajan Gupta, Aarush |
| author_facet | Agarwal, Rajan Gupta, Aarush |
| contents | Instruction-tuned Large Language Models (LLMs) underperform on low resource, non-Latin scripts due to tokenizer fragmentation and weak cross-lingual coupling. We present LLINK (Latent Language Injection for Non-English Knowledge), a compute efficient language-as-modality method that conditions an instruction-tuned decoder without changing the tokenizer or retraining the decoder. First, we align sentence embeddings from a frozen multilingual encoder to the decoder's latent embedding space at a reserved position via a lightweight contrastive projector. Second, the vector is expanded into K soft slots and trained with minimal adapters so the frozen decoder consumes the signal. LLINK substantially improves bilingual retrieval and achieves 81.3% preference over the base model and 63.6% over direct fine-tuning in LLM-judged Q&A evaluations. We further find that improvements can be attributed to reduced tokenization inflation and a stronger cross lingual alignment, despite the model having residual weaknesses in numeric fidelity. Treating low resource languages as a modality offers a practical path to stronger cross-lingual alignment in lightweight LLMs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_27254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Languages are Modalities: Cross-Lingual Alignment via Encoder Injection Agarwal, Rajan Gupta, Aarush Computation and Language Artificial Intelligence Machine Learning Instruction-tuned Large Language Models (LLMs) underperform on low resource, non-Latin scripts due to tokenizer fragmentation and weak cross-lingual coupling. We present LLINK (Latent Language Injection for Non-English Knowledge), a compute efficient language-as-modality method that conditions an instruction-tuned decoder without changing the tokenizer or retraining the decoder. First, we align sentence embeddings from a frozen multilingual encoder to the decoder's latent embedding space at a reserved position via a lightweight contrastive projector. Second, the vector is expanded into K soft slots and trained with minimal adapters so the frozen decoder consumes the signal. LLINK substantially improves bilingual retrieval and achieves 81.3% preference over the base model and 63.6% over direct fine-tuning in LLM-judged Q&A evaluations. We further find that improvements can be attributed to reduced tokenization inflation and a stronger cross lingual alignment, despite the model having residual weaknesses in numeric fidelity. Treating low resource languages as a modality offers a practical path to stronger cross-lingual alignment in lightweight LLMs. |
| title | Languages are Modalities: Cross-Lingual Alignment via Encoder Injection |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.27254 |