Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=
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LLM confidence for social science text measurements is poorly calibrated across models, and a soft-label distillation pipeline reduces expected calibration error by 43% and Brier score by 34%.
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Language-Induced Priors for Domain Adaptation
Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement
LLM confidence for social science text measurements is poorly calibrated across models, and a soft-label distillation pipeline reduces expected calibration error by 43% and Brier score by 34%.