SUTRA：可扩展的多语言语言模型架构
SUTRA: Scalable Multilingual Language Model Architecture
AI总结：
该研究提出 SUTRA 多语言 LLM 架构，通过解耦语言与概念处理并采用混合专家机制，在 50 多种语言及 MMLU 多语言任务上实现更高效、更准确的在线问答。
摘要：
本文介绍 SUTRA，一种能够理解、推理并生成 50 多种语言文本的多语言大型语言模型架构。SUTRA 的设计独特地将核心概念理解与特定语言处理解耦，从而促进可扩展且高效的多语言对齐与学习。通过在语言和概念处理中均采用 Mixture of Experts（混合专家）框架，SUTRA 展现出计算效率与响应能力。大量评估表明，在用于多语言任务的主流 Massive Multitask Language Understanding（MMLU，大规模多任务语言理解）基准上，SUTRA 比 GPT-3.5、Llama2 等现有模型高出 20–30%。SUTRA 模型也是在线 LLM，能够利用互联网知识提供无幻觉、符合事实且最新的回答，同时保留其多语言能力。此外，我们探讨了该架构对多语言 AI 未来的更广泛影响，强调其有可能在全球范围内普及 AI 技术，并提升以非英语语言为主地区的 AI 公平性和实用性。研究结果表明，SUTRA 不仅填补了多语言模型能力方面的关键空白，也为 AI 应用的运行效率和可扩展性建立了新的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
