On the Limitations of Fine-tuned Judge Models for LLM Evaluation

Arxiv Link - 2024-06-17 12:10:34

Abstract

Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs. Many studies have employed proprietary close-source models, especially GPT-4, as the evaluator. Alternatively, other works have fine-tuned judge models based on open-source LLMs as the evaluator. While the fine-tuned judge models are claimed to achieve comparable evaluation capability with GPT-4, in this study, we conduct an empirical study of judge models. Our findings indicate that although the fine-tuned judge models achieve high performance on in-domain test sets, even surpassing GPT-4, they underperform GPT-4 across several dimensions, including generalizability, fairness, aspect-specific evaluation, and scalability. We also reveal that the fine-tuned judge model inherently operates as a task-specific classifier, consequently imposing the limitations. Finally, we propose an effective indicator to measure the reliability of fine-tuned judges, with the aim of maximizing their utility in LLM evaluation.

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🚀 Just in: A new study delves into the evaluation of Large Language Models (LLMs) using fine-tuned judge models. Findings reveal key insights on performance, generalizability, fairness, and scalability in comparison to using GPT-4 as the evaluator. Dive deeper into the research here: Read more #LLM #AI #NLP #Research #GPT4 #TechInnovation	🚀 New research alert! Discover the latest insights on utilizing Large Language Models for evaluation and fine-tuning judge models in AI. Find out more about the study's findings and proposed indicators for maximizing utility in LLM evaluation: http://arxiv.org/abs/2403.02839v2 #AI #LLM #NLP #Research #TechInnovation

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