信息技术-计算机行业大模型系列报告(一)：Transformer架构的过去、现在和未来

Investment Rating - The report maintains a "Positive" investment rating for the industry [1]. Core Insights - The Transformer architecture, introduced by Google Brain in 2017, has revolutionized natural language processing and is now a foundational framework for various AI applications, showcasing significant advantages in speed and long-distance dependency modeling [5][16]. - Despite its strengths, the Transformer architecture faces limitations, particularly in computational complexity and resource demands, which grow quadratically with input sequence length [31][33]. - Future developments in the industry may involve either enhancing the existing Transformer architecture or exploring entirely new frameworks to overcome its limitations [34]. Summary by Sections 1. Transformer Architecture: Past and Present - The architecture is inspired by human cognitive processes, particularly the attention mechanism, which allows efficient information processing [5][9]. - The self-attention mechanism enables the model to focus on key elements within input sequences, significantly improving understanding and processing capabilities [10][19]. 2. Future of Transformer Architecture - The architecture's computational complexity is a major challenge, with the self-attention mechanism's complexity scaling with the square of the sequence length [31][33]. - Potential challengers to the Transformer architecture include new models like RetNet, Mamba, RWKV, and Hyena, each offering unique advantages and addressing specific limitations of the Transformer [34][35]. 3. Investment Recommendations - Short-term investment opportunities are identified in foundational sectors such as data processing and AI model development, with companies like Yingda, Haizhi, and others being highlighted for their innovative contributions [5][29].