The emergence of Large Language Models (LLMs) have fundamentally altered the way we interact with digital systems and have led to the pursuit of LLM powered AI agents to assist in daily workflows. LLMs, whilst powerful and capable of demonstrating some emergent properties, are not logical reasoners and often struggle to perform well at all sub-tasks carried out by an AI agent to plan and execute a workflow. While existing studies tackle this lack of proficiency by generalised pretraining at a huge scale or by specialised fine-tuning for tool use, we assess if a system comprising of a coalition of pretrained LLMs, each exhibiting specialised performance at individual sub-tasks, can match the performance of single model agents. The coalition of models approach showcases its potential for building robustness and reducing the operational costs of these AI agents by leveraging traits exhibited by specific models. Our findings demonstrate that fine-tuning can be mitigated by considering a coalition of pretrained models and believe that this approach can be applied to other non-agentic systems which utilise LLMs.

翻译：大型语言模型（LLMs）的出现从根本上改变了我们与数字系统交互的方式，并推动了基于LLM的人工智能代理在日常工作流程中的辅助应用。尽管LLM功能强大且能展现出某些涌现特性，但它们并非逻辑推理器，在执行人工智能代理为规划与实施工作流程所承担的各项子任务时常常表现欠佳。现有研究通常通过大规模通用预训练或针对工具使用的专门微调来解决这一能力缺陷，而本文则评估了由多个预训练LLM组成的协作系统——其中每个模型在特定子任务上均展现出专业化性能——能否达到单模型代理的性能水平。模型联盟方法通过利用特定模型展现的特性，展现了其在增强人工智能代理鲁棒性和降低运营成本方面的潜力。我们的研究结果表明，通过采用预训练模型联盟可以减轻对微调的依赖，并相信该方法可推广至其他使用LLM的非代理型系统。