让多个大模型自动选择最佳问答策略，提升效率与准确性。
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
- 根据问题特征动态选择大模型间的协作方式。
- 复杂问题用多步推理，简单问题仅需快速查找。
- 适合需要灵活应对多种问题的智能问答系统。
在问答任务中，不同问题需采用不同应答策略：部分只需简单查表，另一些则需多步复杂推理。这一观察促使我们开发一种动态方法，能为每个问题自适应地选择最合适的策略，从而构建更高效、更广泛的问答系统。为此，本文基于多大语言模型（LLM）协同技术，将自适应问答建模为动态调度问题，定义为上下文相关的多臂老虎机问题。其中，上下文由问题特征决定，动作空间为不同大模型代理间的通信图配置。我们训练线性上置信界模型，学习不同问题类型与其对应最优多模型通信图之间的映射关系。实验表明，该方案在多模块问答系统中可行，既能发挥复杂策略的优势，又在简单问题时避免其开销。
原文摘要 · Abstract (English)
In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-step reasoning to be answered adequately. This observation motivates the development of a dynamic method that adaptively selects the most suitable QA strategy for each question, enabling more efficient and effective systems capable of addressing a broader range of question types. To this aim, we build on recent advances in the orchestration of multiple large language models (LLMs) and formulate adaptive QA as a dynamic orchestration challenge. We define this as a contextual multi-armed bandit problem, where the context is defined by the characteristics of the incoming question and the action space consists of potential communication graph configurations among the LLM agents. We then train a linear upper confidence bound model to learn an optimal mapping between different question types and their corresponding optimal multi-LLM communication graph representation. Our experiments show that the proposed solution is viable for adaptive orchestration of a QA system with multiple modules, as it combines the superior performance of more complex strategies while avoiding their costs when simpler strategies suffice.
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