Preprint reports survey estimation gains equivalent to 29-39% fewer respondents

A preprint reports that using language models to extract structural information from questionnaire wording improved estimates from small survey samples. With 50 respondents, its Semantic-Empirical Updating Framework achieved precision comparable to conventional confirmatory factor analysis with 70-82 respondents, equivalent to 29-39% fewer respondents at the specified benchmark. The framework converts model-generated paraphrases and text embeddings into statistical starting assumptions about how questions relate to underlying traits. Researchers evaluated it using a 38-item survey of 617 industrial-park stakeholders and two public surveys. The reported reduction concerns precision in estimating those relationships, rather than replacing empirical validation.