📄 中文摘要
大型语言模型（LLM）架构的多样性和发展趋势引起了广泛关注。不同的架构在处理自然语言任务时展现出各自的优势与局限性。通过对多种LLM架构的比较，分析了它们在性能、效率和应用场景等方面的差异。当前的研究重点包括如何优化模型结构以提高理解和生成能力，以及如何在不同的应用中实现更好的效果。随着技术的不断进步，LLM的架构也在不断演变，推动着人工智能领域的创新与发展。
大型语言模型架构画廊
发布: 2026年3月15日
📄 English Summary
LLM Architecture Gallery
The diversity and development trends of Large Language Model (LLM) architectures have garnered significant attention. Different architectures exhibit their own strengths and limitations when handling natural language tasks. A comparative analysis of various LLM architectures highlights differences in performance, efficiency, and application scenarios. Current research focuses on optimizing model structures to enhance understanding and generation capabilities, as well as achieving better outcomes in diverse applications. As technology continues to advance, the architectures of LLMs are evolving, driving innovation and development in the field of artificial intelligence.