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arXiv · 2408.09235

Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA

Sher Badshah·Hassan Sajjad
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Abstract

The emergence of Large Language Models (LLMs) as chat assistants capable of generating human-like conversations has amplified the need for robust evaluation methods, particularly for open-ended tasks. Conventional metrics such as EM and F1, while useful, are inadequate for capturing the full semantics and contextual depth of such generative outputs. We propose a reference-guided verdict method that automates the evaluation process by leveraging multiple LLMs as judges. Through experiments on free-form question-answering tasks, we demonstrate that combining multiple models improves the reliability and accuracy of evaluations, especially in tasks where a single model may struggle. The results indicate a strong correlation with human evaluations, establishing the proposed method as a reliable alternative to traditional metrics.

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Sher Badshah, Hassan Sajjad. 2024-08-17. Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA. https://arxiv.org/abs/2408.09235

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