Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants

• URL: http://arxiv.org/abs/2406.18675v2
• Date: Tue, 16 Jul 2024 00:13:09 GMT
• Title: Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
• Authors: Minhwa Lee, Zae Myung Kim, Vivek Khetan, Dongyeop Kang,
• Abstract summary: Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. We propose an approach of human-AI collaborative taxonomy development to perform as a guideline for domain-specific writing assistants.
• Score: 17.088117195986758
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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