人机协同分类法构建：以专业特定写作助手为例的案例研究
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
AI总结：
本研究针对LLM在商业领域写作中理解不足的问题，提出人机协同分类法构建方法，通过专家迭代反馈与多次交互完善分类法，以提升领域特定写作助手的效能。
摘要：
大型语言模型（LLMs）已在多种写作任务中为人类提供辅助，包括文本修订和故事生成。然而，它们在支持特定领域写作方面的有效性，尤其是在商业语境中，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LLMs在理解此类特定领域写作细微差别方面的局限性。为弥补这一差距，我们提出了一种人机协同分类法开发方法，作为特定领域写作助手的指导准则。该方法整合了领域专家的迭代反馈以及专家与LLMs之间的多次交互，以完善分类法。通过更大规模的实验，我们旨在验证这一方法论，从而改进基于LLM的写作辅助，使其满足不同利益相关者需求的独特要求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
