标题： 哈斯基：一种统一的、开源的语言代理，用于多步骤推理 显示英文标题

标题： Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning

摘要： 语言代理通过使用工具精确执行每一步来完成复杂任务。 然而，大多数现有的代理基于专有模型或设计用于特定任务，例如数学或多跳问答。 我们介绍了Husky，一个全面的开源语言代理，它学习在统一的动作空间中进行推理，以解决涉及数值、表格和基于知识推理的多样化复杂任务。 Husky在两个阶段之间迭代：1）生成为解决给定任务而采取的下一步动作，2）使用专家模型执行该动作并更新当前解决方案状态。 我们确定了一个全面的动作本体论，用于解决复杂任务，并整理了高质量的数据来训练执行这些动作的专家模型。 我们的实验表明，Husky在14个评估数据集上优于先前的语言代理。 此外，我们引入了HuskyQA，一个新的评估集，它对语言代理进行混合工具推理的压力测试，重点在于检索缺失的知识和执行数值推理。 尽管使用了7B模型，Husky在这些任务上的表现与甚至超过了GPT-4等前沿语言模型，展示了我们全面方法在解决复杂推理问题方面的有效性。 我们的代码和模型可在https://github.com/agent-husky/Husky-v1获取。 显示英文摘要

摘要： Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific tasks, such as mathematics or multi-hop question answering. We introduce Husky, a holistic, open-source language agent that learns to reason over a unified action space to address a diverse set of complex tasks involving numerical, tabular, and knowledge-based reasoning. Husky iterates between two stages: 1) generating the next action to take towards solving a given task and 2) executing the action using expert models and updating the current solution state. We identify a thorough ontology of actions for addressing complex tasks and curate high-quality data to train expert models for executing these actions. Our experiments show that Husky outperforms prior language agents across 14 evaluation datasets. Moreover, we introduce HuskyQA, a new evaluation set which stress tests language agents for mixed-tool reasoning, with a focus on retrieving missing knowledge and performing numerical reasoning. Despite using 7B models, Husky matches or even exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of our holistic approach in addressing complex reasoning problems. Our code and models are available at https://github.com/agent-husky/Husky-v1.