用AI复现田野对话，让研究者与研究对象持续“互动”
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters
- 用RAG技术将访谈和观察文本注入聊天机器人，生成虚拟对话伙伴
- 实验显示可延长田野接触时间，激发新分析视角
- 适合做质性研究的学者，尤其关注人机协作与研究伦理者
本文提出“合成对话者”概念，即通过检索增强生成（RAG）技术，将民族志文本材料（访谈与观察记录）输入开源大语言模型，构建可对话的虚拟研究伙伴。我们基于三个项目的民族志数据，探索两个问题：RAG能否有效消化民族志材料并充当研究对话者？若可，合成对话者是否能延长田野接触、拓展分析深度？通过构建过程反思与合作式实验工作坊，结果表明RAG可有效整合民族志资料，形成持续但充满张力的田野互动体验，使研究者部分重现并重新访问田野互动，同时催生新的分析洞见。合成对话者能产生协作性、模糊性与偶然性的研究时刻。
原文摘要 · Abstract (English)
This paper introduces "Synthetic Interlocutors" for ethnographic research. Synthetic Interlocutors are chatbots ingested with ethnographic textual material (interviews and observations) by using Retrieval Augmented Generation (RAG). We integrated an open-source large language model with ethnographic data from three projects to explore two questions: Can RAG digest ethnographic material and act as ethnographic interlocutor? And, if so, can Synthetic Interlocutors prolong encounters with the field and extend our analysis? Through reflections on the process of building our Synthetic Interlocutors and an experimental collaborative workshop, we suggest that RAG can digest ethnographic materials, and it might lead to prolonged, yet uneasy ethnographic encounters that allowed us to partially recreate and re-visit fieldwork interactions while facilitating opportunities for novel analytic insights. Synthetic Interlocutors can produce collaborative, ambiguous and serendipitous moments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。