Cross-Lingual Alignment for Decoder-Only Models using MoE Routers
Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual tokenization. However, a growing amount of research suggests that even in LLMs, higher cross-lingual representational alignment leads to improved cross-lingual transfer. In this paper, we propose a novel approach to reimagine cross-lingual contrastive learning given the architectural constraints of modern LLMs. Rather than applying an auxiliary alignment loss on hidden states, we propose using the outputs of the mixture-of-experts (MoE) routers as the target for alignment. Rou...
| Authors | Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 October 2026 |
| Last updated at source | 2 October 2026 |
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