Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 SUTRA Approach 3.1 What is SUTRA? 3.1 What is SUTRA? 3.2 Architecture 3.2 Architecture 3.3 Training Data 3.3 Training Data 4 Training Multilingual Tokenizers 4 Training Multilingual Tokenizers 5 Multilingual MMLU 5.1 Massive Multitask Language Understanding 5.1 Massive Multitask Language Understanding 5.2 Extending MMLU to Multiple Languages and 5.3 Consistent Performance across Languages 5.2 Extending MMLU to Multiple Languages and 5.3 Consistent Performance across Languages 5.4 Comparing with leading models for Multilingual Performance 5.4 Comparing with leading models for Multilingual Performance 6 Quantitative Evaluation for Real-Time Queries 6 Quantitative Evaluation for Real-Time Queries 7 Discussion and Conclusion, and References 7 Discussion and Conclusion, and References 7 Discussion and Conclusion Looking ahead, the SUTRA paves the way for the development of phonetic models (approach for SUTRA-Dhvanim), which benefits from the clear separation between concept modeling and language learning. By replacing the NMT decoder with a phonetic decoder, we enable the generation of phonetic responses for more seamless integration with speech models. Our next frontier for optimization is to examine the accuracy and performance impact of structured sparsity and int4 precision, which could significantly reduce SUTRA’s GPU memory footprint and yield with improvements in latency. This research has introduced SUTRA, a state-of-the-art multilingual conversational language model, showcasing its superior ability to handle multiple languages with remarkable efficiency and performance. SUTRA is already proficient in 31 languages across multiple tasks, as detailed in Table 10, and is being extended to support over 50 languages. Unlike its predecessors, which struggle with the nuanced requirements of multi-language understanding, SUTRA exhibits a robust proficiency that is evident across a range of linguistic contexts. This is particularly notable in its application to languages with fewer resources available for model training, which traditionally lag in performance metrics. The innovative architecture of SUTRA, with its decoupled concept and language processing, allows for a scalable and flexible approach to language model training. This not only opens the door for more equitable representation of less commonly spoken languages but also ensures that the quality of interaction remains high across all languages. The efficient tokenization strategy of SUTRA, reducing token fertility for non-English languages, also points to potential cost reductions in deploying AI in multi-language environments, a notable consideration for global accessibility. In conclusion, SUTRA sets a new precedent for multilingual language models by delivering high performance and efficiency without sacrificing linguistic diversity. Its architecture, which mirrors human cognitive development by separating concept understanding from linguistic expression, allows for a more natural and extensive language comprehension. This breakthrough bears significant implications for the global adoption and application of AI, paving the way for more inclusive and equitable access to technology across language barriers. References Tom B. Brown et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. Roger Jia et al. Bias in multilingual models: The case for linguistic equity in ai. In Conference on Neural Information Processing Systems (NeurIPS), 2019. Alexis Conneau et al. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116, 2020. Linda Smith et al. 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Kmmlu: Measuring massive multitask language understanding in korean. arXiv preprint arXiv:2402.11548, 2024. Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, et al. Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149, 2023. Rakuten Group, Aaron Levine, Connie Huang, Chenguang Wang, Eduardo Batista, Ewa Szymanska, Hongyi Ding, Hou Wei Chou, Jean-François Pessiot, Johanes Effendi, et al. Rakutenai-7b: Extending large language models for japanese. arXiv preprint arXiv:2403.15484, 2024. About Two Platforms About Two Platforms Two Platforms (TWO) is a tech startup that aims to redefine Human-AI Interaction, and is at the forefront of the next generation of AI that is visual and immersive. TWO is building consumer AI apps and services powered by its proprietary Gen-AI models. TWO is headquartered in Silicon Valley with offices in Seoul and Mumbai. Authors:
(1) Abhijit Bendale, Two Platforms (abhijit@two.ai);
(2) Michael Sapienza, Two Platforms (michael@two.ai);
(3) Steven Ripplinger, Two Platforms (steven@two.ai);
(4) Simon Gibbs, Two Platforms (simon@two.ai);
(5) Jaewon Lee, Two Platforms (jaewon@two.ai);
(6) Pranav Mistry, Two Platforms (pranav@two.ai). Authors: Authors: (1) Abhijit Bendale, Two Platforms (abhijit@two.ai); (2) Michael Sapienza, Two Platforms (michael@two.ai); (3) Steven Ripplinger, Two Platforms (steven@two.ai); (4) Simon Gibbs, Two Platforms (simon@two.ai); (5) Jaewon Lee, Two Platforms (jaewon@two.ai); (6) Pranav Mistry, Two Platforms (pranav@two.ai). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv

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