Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
<|reference_start|>Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants: 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.<|reference_end|>
@article{lee2024human-ai,
    title={Human-AI Collaborative Taxonomy Construction: A Case Study in
  Profession-Specific Writing Assistants},
    author={Minhwa Lee, Zae Myung Kim, Vivek Khetan and Dongyeop Kang},
    journal={arXiv preprint arXiv:2406.18675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18675},
    primaryClass={cs.HC cs.AI cs.CL}
}