A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE

In three linesMethod to expand LLMs to new languages without costly alignment phase. Converts dense model to Mixture-of-Experts architecture with language-specific experts, then transfers alignment capabilities via post-training delta fusion. Improves performance on new languages while preserving original abilities.

Summary generated by Claude — human-verified