Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement
Large language models (LLMs) are increasingly used in social science as scalable measurement tools for converting unstructured text into variables that can enter standard empirical designs. Measurement validity demands more than high average accuracy, which requires well calibrated confidence that faithfully reflects the empirical probability of each measurement being correct. This paper studies the model miscalibration in LLM-based social science measurement. We begin with a case study on FOMC and show that confidence based filtering can change downstream regression estimates when LLM confidence is miscalibrated. We then audit calibration across 14 social science constructs covering both...
| Authors | Jinyuan Wang, Ningyuan Deng, Yi Yang |
|---|---|
| Primary category | cs.AI |
| Published | 12 May 2026 |
| Last updated at source | 2 June 2026 |
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