  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.
