Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2024
- DOI:
- 10.48550/arXiv.2406.18675
- arXiv:
- arXiv:2406.18675
- Bibcode:
- 2024arXiv240618675L
- Keywords:
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- Computer Science - Human-Computer Interaction;
- Computer Science - Artificial Intelligence;
- Computer Science - Computation and Language
- E-Print:
- Accepted to CHI 2024 In2Writing Workshop