大语言模型如何作为定性实验室：理论原型构建与假设生成
The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models
AI 导读
一篇 arXiv 论文探讨用 LLM 模拟社会学角色扮演来生成质性研究假设。作者用"气候接受矩阵"构建四个虚构人物，让其回应禁售燃油车和气候正义进课程两类话语框架，分析 32 条模型生成的回应。实验显示角色扮演可细化理论预设并提出研究路径，但对提示词构造敏感，定性实验室应作为启发工具而非用人工数据检验理论。
正文 · 原文
Abstract:The use of Large Language Models (LLMs) to simulate social actors has garnered attention for its implications for the epistemic status of discourses generated through such means. This paper considers the more modest question of whether sociological role-playing can serve as a heuristic instrument in the generation of qualitative hypotheses. We take as a case study the development of four divergent fictional characters using a theoretically informed Matrix of Climate Reception. These characters are asked to respond to a series of discourse frames covering two topics: a proposed ban on the sale of new gasoline-powered cars, and the inclusion of climate justice in the school curriculum. We then analyse the 32 model-generated responses for their commonalities, situational differences and divergences from the presuppositions underlying the characters' profiles. The experiment illustrates how LLMs can be used to flesh out theoretical presuppositions into contextual narratives and to propose avenues of further inquiry. It also shows that role-playing can generate considerable similarity between characters and is sensitive to how prompts are constructed, warning against the confusion of discursive coherence with sociological data. Role-playing is argued to be just one of several possible ways to use LLMs for generating hypotheses, with the heuristic specificity of this procedure compared with other methods of direct LLM-based reasoning or with other techniques remaining an open issue. The qualitative laboratory is thus conceived of as a heuristic instrument for provoking reflection and refining questions, not for testing theories with artificial data.
| Comments: | 27 pages, 3 tables. Unpublished manuscript |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Multiagent Systems (cs.MA) |
| ACM classes: | J.4; I.6.8; I.2.7 |
| Cite as: | arXiv:2601.00797 [cs.CL] |
| (or arXiv:2601.00797v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.00797 arXiv-issued DOI via DataCite |
Submission history
From: Hugues Draelants [view email]
[v1]
Tue, 25 Nov 2025 08:31:48 UTC (555 KB)
[v2]
Thu, 12 Nov 2026 18:19:34 UTC (420 KB)
来源：arXiv cs.AI · arxiv.org