SUTRA: Scalable Multilingual Language Model Architecture
Authors: name: Abhijit Bendale, name: Michael Sapienza, name: Steven Ripplinger, name: Simon Gibbs, name: Jaewon Lee, name: Pranav Mistry

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.