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.

翻译：大型语言模型（LLM）已在多项写作任务中为人类提供协助，包括文本修订和故事生成。然而，其在支持领域特定写作（尤其是在商业语境中）方面的有效性相对较少被探索。我们与行业专业人士进行的形成性研究揭示了当前LLM在理解此类领域特定写作细微差别方面的局限性。为弥补这一不足，我们提出了一种人机协同分类体系开发方法，作为领域特定写作助手的指导原则。该方法整合了领域专家的迭代反馈以及专家与LLM之间的多次交互，以完善分类体系。通过更大规模的实验，我们旨在验证此方法，从而改进基于LLM的写作辅助，使其能够满足不同利益相关者需求的独特要求。