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- Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers22
Babel is introduced, an open multilingual LLM that covers the top 25 languages by number of speakers, supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs and sets a new standard for open multilingual LLMs.
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- SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages13
- Unnatural Languages Are Not Bugs but Features for LLMs8
This work demonstrates that unnatural languages - strings that appear incomprehensible to humans but maintain semantic meanings for LLMs - contain latent features usable by models, and demonstrates that models fine-tuned on unnatural versions of instruction datasets perform on-par with those trained on natural language.
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- Multilingual Fine-Tuning via Localized Gradient Conflict Resolution–
Bucket-Level MOO is introduced, a scalable distributed framework that applies gradient-based MOO algorithms locally, within the parameter buckets of distributed training, that enables conflict-aware updates without ever materializing a full gradient vector for any objective, and improves multilingual performance over standard fine-tuning.
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