The advent of ChatGPT and similar large language models (LLMs) has revolutionized the human-AI interaction and information-seeking process. Leveraging LLMs as an alternative to search engines, users can now access summarized information tailored to their queries, significantly reducing the cognitive load associated with navigating vast information resources. This shift underscores the potential of LLMs in redefining information access paradigms. Drawing on the foundation of task-focused information retrieval and LLMs' task planning ability, this research extends the scope of LLM capabilities beyond routine task automation to support users in navigating long-term and significant life tasks. It introduces the GOLF framework (Goal-Oriented Long-term liFe tasks), which focuses on enhancing LLMs' ability to assist in significant life decisions through goal orientation and long-term planning. The methodology encompasses a comprehensive simulation study to test the framework's efficacy, followed by model and human evaluations to develop a dataset benchmark for long-term life tasks, and experiments across different models and settings. By shifting the focus from short-term tasks to the broader spectrum of long-term life goals, this research underscores the transformative potential of LLMs in enhancing human decision-making processes and task management, marking a significant step forward in the evolution of human-AI collaboration.

翻译：ChatGPT及类似大型语言模型（LLMs）的出现彻底改变了人机交互与信息检索方式。利用LLMs作为搜索引擎的替代方案，用户现已能获取针对其查询定制的汇总信息，显著降低了在庞大数据资源中导航的认知负荷。这一转变凸显了LLMs在重塑信息获取范式中的潜力。本研究基于任务导向型信息检索理论与LLMs的任务规划能力，将LLMs的应用范围从常规任务自动化拓展至支持用户完成长期重大生活任务。研究提出GOLF框架（Goal-Oriented Long-term liFe tasks），通过目标导向与长期规划机制，聚焦提升LLMs在重大生活决策中的辅助能力。方法体系包括：通过综合仿真研究测试框架效能，结合模型评估与人工评估构建长期生活任务数据集基准，并开展跨模型与跨场景对比实验。通过将研究焦点从短期任务转向长期生活目标的宏观视角，本研究揭示了LLMs在增强人类决策过程与任务管理中的变革性潜力，标志着人机协同发展迈出关键一步。