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LLM confidence miscalibration impacts social science research

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM confidence miscalibration impacts social science research

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

  1. arXiv cs.AI TIER_1 English(EN) · Jinyuan Wang, Ningyuan Deng, Yi Yang ·

    Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement

    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…

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