Large Language Models (LLMs) have assisted humans in several writing tasks,
including text revision and story generation. However, their effectiveness in
supporting domain-specific writing, particularly in business contexts, is
relatively less explored. Our formative study with industry professionals
revealed the limitations in current LLMs' understanding of the nuances in such
domain-specific writing. To address this gap, we propose an approach of
human-AI collaborative taxonomy development to perform as a guideline for
domain-specific writing assistants. This method integrates iterative feedback
from domain experts and multiple interactions between these experts and LLMs to
refine the taxonomy. Through larger-scale experiments, we aim to validate this
methodology and thus improve LLM-powered writing assistance, tailoring it to
meet the unique requirements of different stakeholder needs.

通过人工智能协作共同开发的领域特定写作助手分类系统来解决大型语言模型在商业背景下理解能力的限制，为不同利益相关者提供定制化的大型语言模型驱动的写作辅助功能。