Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant exploration in this field, existing language agent systems still struggle with costly, non-reproducible data reliance and face the challenge of compelling a single model for multiple functions. To this end, we introduce AutoAct, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models (e.g., GPT-4). Given limited data with a tool library, AutoAct first automatically synthesizes planning trajectories without any assistance from humans or strong closed-source models. Then, AutoAct leverages a division-of-labor strategy to automatically differentiate based on the target task information and synthesized trajectories, producing a sub-agent group to complete the task. We conduct comprehensive experiments with different LLMs, which demonstrates that AutoAct yields better or parallel performance compared to various strong baselines. Further analysis demonstrates the effectiveness of the division-of-labor strategy, with the trajectory quality generated by AutoAct generally outperforming that of others. Code will be available at https://github.com/zjunlp/AutoAct.

翻译：语言智能体通过结合外部工具进行规划，已在各类复杂问答任务上取得了显著性能。尽管该领域持续探索，现有语言智能体系统仍受限于成本高昂、不可复现的数据依赖，并面临迫使单一模型承担多重功能的挑战。为此，我们提出了AutoAct，一种面向问答任务的自动智能体学习框架，其不依赖于大规模标注数据及闭源模型（如GPT-4）生成的合成规划轨迹。在给定有限数据和工具库的条件下，AutoAct首先无需人类或强大闭源模型的协助，自动合成规划轨迹。随后，AutoAct采用分工策略，基于目标任务信息及合成轨迹自动进行差异化处理，生成一个子智能体群以完成任务。我们在不同大语言模型上进行了全面实验，结果表明AutoAct相比多种强基线方法取得了更优或相当的性能。进一步分析验证了分工策略的有效性，AutoAct生成的轨迹质量总体上优于其他方法。代码将在https://github.com/zjunlp/AutoAct 发布。