Large Language Models (LLMs) have demonstrated remarkable capabilities in solving various tasks, yet they often struggle with comprehensively addressing complex and vague problems. Existing approaches, including multi-agent LLM systems, offer solutions to certain challenges but still require manual setup and lack scalability. To address this gap, we propose a novel approach leveraging decomposition to enable LLMs to tackle vague problems effectively. Our approach involves an orchestrating LLM that interacts with users to understand the problem and then decomposes it into tangible sub-problems. Instead of expecting the LLM to solve the entire problem in one go, we train it to ask follow-up questions to gain a deeper understanding of the user's requirements. Once the problem is adequately understood, the orchestrating LLM divides it into smaller, manageable sub-problems. Each sub-problem is then assigned to specialized LLM agents or non-LLM functions for resolution. These agents work in parallel to solve their respective sub-problems, with the orchestrating LLM overseeing the process and compiling the solutions into a comprehensive answer for the user. By adopting this decomposition approach, we alleviate the constraints imposed by token limitations on LLM outputs and empower them to provide nuanced solutions to complex and ambiguous problems. Through our approach, we aim to enable LLMs to think and operate more like humans, breaking down complex problems into manageable parts and collaboratively solving them. This not only enhances the problem-solving capabilities of LLMs but also offers a scalable and efficient method for addressing a wide range of real-world challenges.

翻译：大语言模型在解决各类任务中展现出卓越能力，但在全面应对复杂模糊问题时仍存在显著局限。现有方法（包括多智能体大语言模型系统）虽能解决特定挑战，却需要人工配置且缺乏可扩展性。针对这一缺陷，我们提出利用分解策略使大语言模型有效处理模糊问题的新方法。该方法通过一个协调型大语言模型与用户交互理解问题，并将其分解为具体子问题。不同于要求模型一次性解决完整问题，我们训练其通过追问式交互深入理解用户需求。当问题被充分理解后，协调模型将其划分为可管理的子问题单元，每个子问题分配至专用大语言模型智能体或非大语言模型功能模块进行求解。各智能体并行处理对应子问题，由协调模型全程监督并整合求解结果，最终形成综合答案。通过这种分解策略，我们缓解了语言模型输出受令牌限制的约束，使其能够对复杂歧义问题提供精细化解决方案。该方法旨在引导大语言模型模拟人类思维模式，将复杂问题拆解为可管理模块并协同求解，不仅增强其问题解决能力，更为应对广泛现实挑战提供了可扩展的高效方案。