New Method Corrects Bias in LLM-Driven Regression Analysis

Researchers have developed a novel method to correct for biases that arise when using prediction-generated measures, such as those from large language models (LLMs), as explanatory variables in regression analyses. This split-sample instrumental variable approach uses multiple measures created on independent data splits to construct instruments, thereby mitigating estimation biases. The proposed technique is theoretically sound, simple to implement, and does not require additional data, as demonstrated through simulations and applications to legislative outcomes in the German Parliament and political risk in China. AI

IMPACT Provides a statistical correction for using LLM outputs in downstream analysis, improving the reliability of research findings.

RANK_REASON Academic paper proposing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

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• China
• German Bundestag
• large-language models

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• other

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