Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917722623836160 |
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| author | Lee, Minhwa Kim, Zae Myung Khetan, Vivek Kang, Dongyeop |
| author_facet | Lee, Minhwa Kim, Zae Myung Khetan, Vivek Kang, Dongyeop |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_18675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants Lee, Minhwa Kim, Zae Myung Khetan, Vivek Kang, Dongyeop Human-Computer Interaction Artificial Intelligence Computation and Language 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. |
| title | Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.18675 |