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LLMs improve public opinion data imputation via in-context learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for imputing missing public opinion data using large language models (LLMs) through in-context learning (ICL). This approach was tested on survey data and showed consistent error reduction compared to traditional statistical methods like MICE PMM. The best-performing ICL method, utilizing a gpt-oss-120b model with 100 examples, achieved narrower confidence intervals and improved aggregate coverage, particularly under non-random missingness. AI

IMPACT This research demonstrates a novel application of LLMs for improving the accuracy and efficiency of public opinion data imputation, potentially impacting survey methodology and analysis.

RANK_REASON Academic paper detailing a novel application of LLMs for data imputation.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Anna-Carolina Haensch ·

    In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

    Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

    Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling …

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