Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement
A new paper examines the issue of miscalibration in large language models when used for social science research. The study found that LLMs often report confidence scores that do not accurately reflect their correctness, which can impact downstream analysis. Researchers proposed a soft label distillation method to improve calibration in smaller models, showing significant reductions in calibration error. AI
IMPACT Highlights the need for improved LLM calibration in research settings to ensure reliable data extraction and analysis.