面向上下文LLM级联的在线潘多拉魔盒
英文摘要
The paper introduces an 'online Pandora's Box' mechanism for contextual LLM cascading that dynamically selects the most contextually relevant large language model for each task. It proposes a systematic categorization of LLMs to structure the cascading process, optimizing both resource usage and response accuracy. The framework enables real-time adaptability, and experimental results indicate a significant performance boost for LLM systems in various natural language processing applications.
中文摘要
该论文提出一种用于上下文LLM级联的“在线潘多拉魔盒”机制，能够根据任务上下文动态选择最相关的大语言模型。它通过对LLM进行系统分类来结构化级联流程，从而优化资源使用和回应准确性。该框架支持实时适应，实验结果表明能显著提升LLM系统在各类自然语言处理应用中的性能。
关键要点
Introduces an 'online Pandora's Box' mechanism that dynamically selects LLMs based on contextual relevance, improving both efficiency and accuracy.
提出一种“在线潘多拉魔盒”机制，根据上下文相关性动态选择LLM，同时提升效率和准确性。
Proposes a systematic categorization of LLMs to structure the cascading process, enabling more predictable resource utilization.
通过系统分类LLM来结构化级联过程，使资源使用更可控。
Demonstrates that the online framework supports real-time adaptability and significantly enhances performance across NLP tasks.
证明该在线框架支持实时适应，并能在多种NLP任务中显著提升性能。