With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks which necessitate a combination of task planning and the usage of external tools. In this paper, we first propose a structured framework tailored for LLM-based AI Agents and discuss the crucial capabilities necessary for tackling intricate problems. Within this framework, we design two distinct types of agents (i.e., one-step agent and sequential agent) to execute the inference process. Subsequently, we instantiate the framework using various LLMs and evaluate their Task Planning and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings and challenges, our goal is to provide a helpful resource for researchers and practitioners to leverage the power of LLMs in their AI applications. Our study emphasizes the substantial potential of these models, while also identifying areas that need more investigation and improvement.

翻译：随着自然语言处理的最新进展，大语言模型（LLMs）已成为多种现实应用中的强大工具。尽管具有卓越能力，但LLMs固有的生成能力可能不足以处理需要任务规划与外部工具使用相结合完成的复杂任务。在本文中，我们首先提出一个专为基于LLM的AI代理设计的结构化框架，并讨论解决复杂问题所需的关键能力。在此框架内，我们设计了两种不同类型的代理（即单步代理和顺序代理）来执行推理过程。随后，我们使用多种LLMs实例化该框架，并评估其在典型任务上的任务规划与工具使用（TPTU）能力。通过突出关键发现与挑战，我们的目标是为研究人员和从业者提供有益资源，以便在其AI应用中发挥LLMs的潜力。本研究强调这些模型的巨大潜力，同时指出需要进一步探索和改进的领域。