Benchmarking Debiasing Methods for LLM-based Parameter Estimates

2025-06-11

AI Summary

While large language models (LLMs) enable low-cost, high-throughput text annotation, their systematic disagreement with expert annotations introduces bias in regression coefficient and causal effect estimation. Method: This paper conducts the first systematic, real-scale empirical evaluation of two leading de-biasing frameworks—Design-based Supervised Learning (DSL) and Prediction-Powered Inference (PPI)—under limited expert labeling budgets. We model LLM annotation error, analyze the bias–variance trade-off, and identify critical thresholds for expert label quantity. Results: DSL achieves lower bias and higher empirical efficiency across most tasks, yet exhibits weaker cross-dataset stability than PPI. Both methods’ reliability hinges on a minimum number of expert labels. Our findings provide principled guidance for method selection and practical implementation of statistically sound inference in LLM-augmented annotation pipelines.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) offer an inexpensive yet powerful way to annotate text, but are often inconsistent when compared with experts. These errors can bias downstream estimates of population parameters such as regression coefficients and causal effects. To mitigate this bias, researchers have developed debiasing methods such as Design-based Supervised Learning (DSL) and Prediction-Powered Inference (PPI), which promise valid estimation by combining LLM annotations with a limited number of expensive expert annotations. Although these methods produce consistent estimates under theoretical assumptions, it is unknown how they compare in finite samples of sizes encountered in applied research. We make two contributions: First, we study how each method's performance scales with the number of expert annotations, highlighting regimes where LLM bias or limited expert labels significantly affect results. Second, we compare DSL and PPI across a range of tasks, finding that although both achieve low bias with large datasets, DSL often outperforms PPI on bias reduction and empirical efficiency, but its performance is less consistent across datasets. Our findings indicate that there is a bias-variance tradeoff at the level of debiasing methods, calling for more research on developing metrics for quantifying their efficiency in finite samples.

Problem

• Evaluating debiasing methods for LLM-based parameter estimates
• Comparing DSL and PPI performance with limited expert annotations
• Assessing bias-variance tradeoff in debiasing methods' efficiency

Innovation

• Combines LLM annotations with expert annotations
• Compares DSL and PPI debiasing methods
• Analyzes bias-variance tradeoff in finite samples