Fleet of Agents: Coordinated Problem Solving with Large Language Models using Genetic Particle Filtering

• URL: http://arxiv.org/abs/2405.06691v1
• Date: Tue, 7 May 2024 09:36:23 GMT
• Title: Fleet of Agents: Coordinated Problem Solving with Large Language Models using Genetic Particle Filtering
• Authors: Akhil Arora, Lars Klein, Nearchos Potamitis, Roland Aydin, Caglar Gulcehre, Robert West,
• Abstract summary: Fleet of Agents (FoA) is a framework utilizing large language models as agents to navigate through dynamic tree searches. FoA spawns a multitude of agents, each exploring autonomously, followed by a selection phase. We experimentally validate FoA using two benchmark tasks, "Game of 24 and "Mini-Crosswords"
• Score: 10.167121757937062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have significantly evolved, moving from simple output generation to complex reasoning and from stand-alone usage to being embedded into broader frameworks. In this paper, we introduce \emph{Fleet of Agents (FoA)}, a novel framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We experimentally validate FoA using two benchmark tasks, "Game of 24" and "Mini-Crosswords". FoA outperforms the previously proposed Tree-of-Thoughts method in terms of efficacy and efficiency: it significantly decreases computational costs (by calling the value function less frequently) while preserving comparable or even superior accuracy.

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