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

• URL: http://arxiv.org/abs/2408.09235v2
• Date: Tue, 20 Aug 2024 15:12:08 GMT
• Title: Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form Text
• Authors: Sher Badshah, Hassan Sajjad,
• Abstract summary: Large Language Models (LLMs) are capable of generating human-like conversations. Conventional metrics like BLEU and ROUGE are inadequate for capturing the subtle semantics and contextual richness of such generative outputs. We propose a reference-guided verdict method that automates the evaluation process by leveraging multiple LLMs-as-judges.
• Score: 12.879551933541345
• License: http://creativecommons.org/licenses/by/4.0/
• 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 like BLEU and ROUGE, while useful, are increasingly inadequate for capturing the subtle semantics and contextual richness 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 three open-ended question-answering tasks, we demonstrate that combining multiple LLMs-as-judges significantly improves the reliability and accuracy of evaluations, particularly in complex tasks where a single model might struggle. Our findings reveal a strong correlation with human evaluations, establishing our method as a viable and effective alternative to traditional metrics and human judgments, particularly in the context of LLM-based chat assistants where the complexity and diversity of responses challenge existing benchmarks.

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