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（LLM）在解决各类任务中展现出卓越能力，但在全面处理复杂且模糊的问题时仍面临困难。现有方法（包括多智能体LLM系统）虽能应对部分挑战，但仍需人工设置且缺乏可扩展性。为弥补这一不足，我们提出一种利用分解机制的新方法，使LLM能够有效处理模糊问题。该方法通过一个协调型LLM与用户交互以理解问题，进而将其分解为明确子问题。我们不要求LLM一次性解决整个问题，而是训练其通过追问来深入理解用户需求。当问题被充分理解后，协调型LLM将其划分为更小、易处理的子问题。每个子问题被分配给专门的LLM智能体或非LLM功能模块进行求解。这些智能体并行处理各自子问题，协调型LLM则监督整个过程，并将解决方案整合成完整答案反馈给用户。通过采用这种分解方法，我们缓解了LLM输出受令牌数量限制的约束，使其能够为复杂模糊问题提供细致入微的解决方案。我们的研究旨在使LLM能像人类一样思考与运作——将复杂问题拆解为可管理部分并协同求解。这不仅增强了LLM的问题解决能力，也为应对广泛现实挑战提供了可扩展且高效的方法。