Expanding Horizons in HCI Research Through LLM-Driven Qualitative Analysis
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913189376032768 |
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| author | Torii, Maya Grace Murakami, Takahito Ochiai, Yoichi |
| author_facet | Torii, Maya Grace Murakami, Takahito Ochiai, Yoichi |
| contents | How would research be like if we still needed to "send" papers typed with a typewriter? Our life and research environment have continually evolved, often accompanied by controversial opinions about new methodologies. In this paper, we embrace this change by introducing a new approach to qualitative analysis in HCI using Large Language Models (LLMs). We detail a method that uses LLMs for qualitative data analysis and present a quantitative framework using SBART cosine similarity for performance evaluation. Our findings indicate that LLMs not only match the efficacy of traditional analysis methods but also offer unique insights. Through a novel dataset and benchmark, we explore LLMs' characteristics in HCI research, suggesting potential avenues for further exploration and application in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expanding Horizons in HCI Research Through LLM-Driven Qualitative Analysis Torii, Maya Grace Murakami, Takahito Ochiai, Yoichi Human-Computer Interaction Artificial Intelligence How would research be like if we still needed to "send" papers typed with a typewriter? Our life and research environment have continually evolved, often accompanied by controversial opinions about new methodologies. In this paper, we embrace this change by introducing a new approach to qualitative analysis in HCI using Large Language Models (LLMs). We detail a method that uses LLMs for qualitative data analysis and present a quantitative framework using SBART cosine similarity for performance evaluation. Our findings indicate that LLMs not only match the efficacy of traditional analysis methods but also offer unique insights. Through a novel dataset and benchmark, we explore LLMs' characteristics in HCI research, suggesting potential avenues for further exploration and application in the field. |
| title | Expanding Horizons in HCI Research Through LLM-Driven Qualitative Analysis |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2401.04138 |