Recidivism Prediction, Peer Effect Estimation, and Prediction-Powered Inference with LLM Text Measures

The paper introduces a framework for estimating peer effects using multivariate behavioral measures extracted from written text via a large language model (LLM). It demonstrates that LLM embeddings enhance out‑of‑sample recidivism prediction by up to 30% compared to pre‑entry covariates alone, and presents a novel instrumental variable estimator that is √N‑consistent for sparse networks with multidimensional latent homophily. By combining limited human annotations with LLM zero‑shot vectors, the authors develop a prediction‑powered peer inference method that yields de‑biased estimates and reveals significant peer effects in behavioral profiles.

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• llms
• rag
• fine-tuning
• efficiency