Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)

Eugene Yan · 1 year ago

Researchers evaluated large language models used as automated judges to assess the quality of other LLM outputs, comparing approaches like direct scoring, pairwise comparisons, and reference-based evaluation. Studies show LLM-evaluators achieve correlation with humans ranging from 0.3 to 0.9 depending on the task, with larger models (52B+ parameters) approaching performance of finetuned preference models when using chain-of-thought prompting. Organizations can use LLM-evaluators to scale evaluation beyond human annotation while choosing between classification metrics or correlation metrics depending on whether they need binary outputs or ranked assessments.