ICL with LLMs reduces absolute imputation error for survey data versus MICE PMM across MCAR/MAR/MNAR mechanisms and yields narrower intervals with near-nominal coverage.
Sociological Methods & Research , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Audience segmentation restores heterogeneity in LLM social simulations, with moderate granularity and data-driven selection often improving structural and predictive fidelity on U.S. climate-opinion data while no configuration dominates all evaluation dimensions.
LLMs exhibit higher perplexity on far-right and nationalist party texts than social-democratic ones, consistent across models and languages with correlation to translation metrics.
citing papers explorer
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In-Context Learning for the Imputation of Public Opinion Data with Large Language Models
ICL with LLMs reduces absolute imputation error for survey data versus MICE PMM across MCAR/MAR/MNAR mechanisms and yields narrower intervals with near-nominal coverage.
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Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach
Audience segmentation restores heterogeneity in LLM social simulations, with moderate granularity and data-driven selection often improving structural and predictive fidelity on U.S. climate-opinion data while no configuration dominates all evaluation dimensions.
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Large Language Models are Perplexed by some Political Parties
LLMs exhibit higher perplexity on far-right and nationalist party texts than social-democratic ones, consistent across models and languages with correlation to translation metrics.