arXiv:2605.11954v2 Announce Type: replace Abstract: 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 cha
The proliferation of Large Language Models (LLMs) into social science research necessitates immediate attention to their reliability and accuracy, particularly regarding calibration.
Ensuring the validity and trustworthiness of LLM-based measurements is crucial for sound empirical social science and for policy decisions that may be informed by such research.
The focus on miscalibration introduces a new critical assessment framework for LLM applications in social science, moving beyond simple accuracy metrics.
- · AI researchers focused on explainability and calibration
- · Social scientists adopting rigorous LLM validation methods
- · Open-source LLM developers improving model transparency
- · Uncritical adopters of LLM-based measurement
- · Researchers relying solely on LLM accuracy scores
- · Proprietary LLM providers with opaque confidence mechanisms
Increased scrutiny and demand for calibrated confidence scores in LLM outputs used for research.
Development of new methodologies and tools specifically designed to assess and mitigate LLM miscalibration in various domains.
Potentially, a re-evaluation or discounting of past social science research that utilized uncalibrated LLM measurements, leading to new studies and revised conclusions.
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