Large Language Models (LLMs) present an opportunity to create automated assistants that can help users navigate complex tasks. However, existing approaches have limitations in handling conditional logic, integrating knowledge sources, and consistently following instructions. Researchers and industry professionals often employ ad hoc pipelines to construct conversational agents. These pipelines aim to maintain context, address failure cases, and minimize hallucinations, yet frequently fail to achieve these objectives. To this end, we present Genie - a programmable framework for creating task-oriented conversational agents that are designed to handle complex user interactions and knowledge queries. Unlike LLMs, Genie provides reliable grounded responses, with controllable agent policies through its expressive specification, Genie Worksheet. In contrast to dialog trees, it is resilient to diverse user queries, helpful with knowledge sources, and offers ease of programming policies through its declarative paradigm. The agents built using Genie outperforms the state-of-the-art method on complex logic domains in STARV2 dataset by up to 20.5%. Additionally, through a real-user study involving 62 participants, we show that Genie beats the GPT-4 with function calling baseline by 21.1%, 20.1%, and 61% on execution accuracy, dialogue act accuracy, and goal completion rate, respectively, on three diverse real-world domains

翻译：大型语言模型（LLMs）为创建能够帮助用户处理复杂任务的自动化助手提供了机遇。然而，现有方法在处理条件逻辑、整合知识源以及持续遵循指令方面存在局限。研究人员与行业从业者通常采用临时构建的流程来开发对话智能体。这些流程旨在维持上下文、处理故障案例并减少幻觉现象，却往往难以达成这些目标。为此，我们提出Genie——一个用于创建面向任务的对话智能体的可编程框架，该框架专为处理复杂的用户交互与知识查询而设计。与LLMs不同，Genie通过其富有表现力的规范语言Genie Worksheet，能够提供可靠且基于事实的响应，并实现可控的智能体策略。相较于对话树结构，Genie能够灵活应对多样化的用户查询，有效利用知识源，并通过其声明式编程范式简化策略编程流程。基于Genie构建的智能体在STARV2数据集的复杂逻辑领域上，性能超越现有最优方法达20.5%。此外，通过对62名参与者开展的真实用户研究表明，在三个不同的现实领域任务中，Genie在任务执行准确率、对话行为准确率和目标完成率上分别以21.1%、20.1%和61%的优势超越了基于函数调用的GPT-4基线模型。