LLM confidence miscalibration impacts social science research

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.

RANK_REASON Academic paper detailing a specific issue with LLM usage in a research domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DeepSeek-V3.2
• GPT-5-mini
• Jinyuan Wang
• Large language models
• Social science

• paper
• safety

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