title: SUTRA: Scalable Multilingual Language Model Architecture
summary: 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.
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paper_authors.name: Abhijit Bendale
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paper_authors.name: Simon Gibbs
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paper_authors.name: Pranav Mistry
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paper_title: SUTRA: Scalable Multilingual Language Model Architecture
paper_summary: 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.
paper_ai_summary: SUTRA, a multilingual Large Language Model architecture, achieves superior performance on multilingual tasks by decoupling conceptual understanding from language-specific processing using a Mixture of Experts framework.
paper_ai_keywords: Multilingual Large Language Model Mixture of Experts framework Massive Multitask Language Understanding (MMLU)