E. J. Hauer | Searcharxiv

arXiv2024

Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs

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.

cs.MA↗

arXiv2024

Optimal Decision Making Through Scenario Simulations Using Large Language Models

The rapid evolution of Large Language Models (LLMs) has markedly expanded their application across diverse domains, transforming how complex problems are approached and solved. Initially conceived to predict subsequent words in texts, these models have transcended their original design to comprehend and respond to the underlying contexts of queries. Today, LLMs routinely perform tasks that once seemed formidable, such as writing essays, poems, stories, and even developing software code. As their capabilities continue to grow, so too do the expectations of their performance in even more sophisticated domains. Despite these advancements, LLMs still encounter significant challenges, particularly in scenarios requiring intricate decision-making, such as planning trips or choosing among multiple viable options. These tasks often demand a nuanced understanding of various outcomes and the ability to predict the consequences of different choices, which are currently outside the typical operational scope of LLMs. This paper proposes an innovative approach to bridge this capability gap. By enabling LLMs to request multiple potential options and their respective parameters from users, our system introduces a dynamic framework that integrates an optimization function within the decision-making process. This function is designed to analyze the provided options, simulate potential outcomes, and determine the most advantageous solution based on a set of predefined criteria. By harnessing this methodology, LLMs can offer tailored, optimal solutions to complex, multi-variable problems, significantly enhancing their utility and effectiveness in real-world applications. This approach not only expands the functional envelope of LLMs but also paves the way for more autonomous and intelligent systems capable of supporting sophisticated decision-making tasks.

cs.AI↗