How to measure human-LLM judge alignment

This guide explores how to measure alignment between human and large language model (LLM) judges, emphasizing the importance of understanding the consistency and reliability of both human and LLM judgments. It stresses that no single metric can determine the trustworthiness of an LLM judge, necessitating a multi-faceted approach to evaluation. The process involves defining the evaluation task, measuring human-human agreement, comparing human and LLM agreement, and treating the LLM judge as a classifier against a human-created reference. The guide recommends using multiple human annotations to establish a defensible reference, reporting raw and chance-adjusted agreement metrics, and carefully analyzing classification metrics like precision, recall, and F1 scores to identify errors and improve evaluation criteria. The workflow includes collecting representative examples, calibrating human annotations, running LLM evaluations on a held-out dataset, and analyzing disagreements to refine the rubric. Overall, the guide provides a comprehensive framework for ensuring that LLM judges operate within the range of human judgments and highlights the importance of continual improvement through versioning and analysis of evaluator performance.

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