Simulacrum of stories: Examining Large Language Models as Qualitative Research Participants

要旨

The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development–e.g., through surveys, experiments, and interviews—with synthetic research data generated by large language models (LLMs). We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift. Initially skeptical, researchers were surprised to see similar narratives emerge in the LLM-generated data when using the interview probe. However, over several conversational turns, they went on to identify fundamental limitations, such as how LLMs foreclose participants’ consent and agency, produce responses lacking in palpability and contextual depth, and risk delegitimizing qualitative research methods. We argue that the use of LLMs as proxies for participants enacts the surrogate effect, raising ethical and epistemological concerns that extend beyond the technical limitations of current models to the core of whether LLMs fit within qualitative ways of knowing.

受賞
Honorable Mention
著者
Shivani Kapania
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
William Agnew
CMU, Pittsburgh, Pennsylvania, United States
Motahhare Eslami
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Hoda Heidari
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Sarah E. Fox
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI

10.1145/3706598.3713220

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713220

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: HCI Methods and Practices

Annex Hall F203
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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