In this paper, we introduce SUTRA, multilingual Large Language Model
architecture capable of understanding, reasoning, and generating text in over
50 languages. SUTRA's design uniquely decouples core conceptual understanding
from language-specific processing, which facilitates scalable and efficient
multilingual alignment and learning. Employing a Mixture of Experts framework
both in language and concept processing, SUTRA demonstrates both computational
efficiency and responsiveness. Through extensive evaluations, SUTRA is
demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on
leading Massive Multitask Language Understanding (MMLU) benchmarks for
multilingual tasks. SUTRA models are also online LLMs that can use knowledge
from the internet to provide hallucination-free, factual and up-to-date
responses while retaining their multilingual capabilities. Furthermore, we
explore the broader implications of its architecture for the future of
multilingual AI, highlighting its potential to democratize access to AI
technology globally and to improve the equity and utility of AI in regions with
predominantly non-English languages. Our findings suggest that SUTRA not only
fills pivotal gaps in multilingual model capabilities but also establishes a
new benchmark for operational efficiency and scalability in AI applications.

通过引入 SUTRA，一种能够理解、推理和生成超过 50 种语言文本的多语言大型语言模型架构，本文展示了其在多语言任务上超越 GPT-3.5 和 Llama2 等现有模型 20-30% 的结果，并对其未来在多语言人工智能领域的广泛影响进行了探讨。