March 5, 2024Open Access

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Models are Task-specific Classifiers

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Fine-tuned judge models demonstrate high accuracy on specific tasks, yet lack generalizability.
The study compares several judge models for their evaluation capability against GPT4.
Empirical analysis shows task-specific classifiers underperform in fairness metrics compared to GPT4's broader applicability and reliability as a judge model with multiple tasks in mind. This reveals critical insights into language model evaluations.

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Empirical study evaluates fine-tuned large language model judges, revealing limited generalizability and fairness compared to GPT4.

Huang et al. (2024) studied this question.

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