Esko Lehtonen, Tommi Buder-Gröndahl · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29645 · open-access PDF
大型语言模型(LLMs)已被探索为人类调查受访者的替代品。更有希望的应用程序是数据扩展,LLMs预测来自部分人类数据的新答案。然而,问卷项目之间的语义相似性是答案之间的相关性的已知来源。因此,不清楚LLMs是否实际上推断了问答对之间有意义的关系,或使用问题之间的表面水平比较作为一种振奋。
Machine translation (Meta M2M100); the English original follows.
Large language models (LLMs) have been explored as surrogates for human survey respondents. A more promising application is data augmentation, where LLMs predict new responses from partial human data. However, the semantic similarity between questionnaire items is a known source of correlation between answers. It is thus unclear whether LLMs actually infer meaningful relationships between question-response pairs, or use surface-level comparison between questions as a heuristic. To assess this, we evaluated how three LLMs can predict responses to held-out items in two surveys measuring attitude
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