智能投顾的大模型应用，为什么选择了“大小模型协同”？

Core Viewpoint - The financial industry is at the forefront of technological innovation in the era of large models, with the implementation of intelligent investment advisory posing both technical challenges and compliance risks. The company adopts a "collaboration of large and small models" approach to balance performance, accuracy, and compliance [1][2]. Summary by Sections Technical Challenges - The primary technical challenge in implementing large models in investment advisory is avoiding hallucinations and incorrect answers in a high-compliance environment. Direct application of large models carries significant compliance risks due to the high stakes involved in financial decision-making [2][7]. Collaboration of Large and Small Models - The collaboration of large and small models offers two main advantages: 1. It limits the scope of large model responses, focusing on task expansion and framework building, while specialized small models handle in-depth content output, reducing the likelihood of errors [2][3]. 2. It enhances the ratio of response depth to computational power consumption, allowing for quicker and more stable responses from small models without requiring extensive logical reasoning from large models [4][5]. Modular Architecture - The architecture allows for decoupling of large models from foundational models, enabling quick replacement of specific models as needed. This modular approach enhances application stability and growth potential, as well as privacy [6][8]. Practical Applications - The collaboration model has been implemented internally, showing significant improvements in response depth and compliance compared to traditional large models. The system allows seamless transitions between different foundational models, maintaining professional standards [8][9]. Future Trends - The future of AI application architecture in finance is expected to evolve towards a combination of language understanding and tool invocation, with the collaboration of large and small models being part of a broader trend. The integration of LLMs with APIs and RPA will play a crucial role in enhancing operational efficiency [9].