Check-Eval: A Checklist-based Approach for Evaluating Text Quality

• URL: http://arxiv.org/abs/2407.14467v2
• Date: Tue, 10 Sep 2024 14:08:29 GMT
• Title: Check-Eval: A Checklist-based Approach for Evaluating Text Quality
• Authors: Jayr Pereira, Andre Assumpcao, Roberto Lotufo,
• Abstract summary: textscCheck-Eval can be employed as both a reference-free and reference-dependent evaluation method. textscCheck-Eval achieves higher correlations with human judgments compared to existing metrics.
• Score: 3.031375888004876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the quality of text generated by large language models (LLMs) remains a significant challenge. Traditional metrics often fail to align well with human judgments, particularly in tasks requiring creativity and nuance. In this paper, we propose \textsc{Check-Eval}, a novel evaluation framework leveraging LLMs to assess the quality of generated text through a checklist-based approach. \textsc{Check-Eval} can be employed as both a reference-free and reference-dependent evaluation method, providing a structured and interpretable assessment of text quality. The framework consists of two main stages: checklist generation and checklist evaluation. We validate \textsc{Check-Eval} on two benchmark datasets: Portuguese Legal Semantic Textual Similarity and \textsc{SummEval}. Our results demonstrate that \textsc{Check-Eval} achieves higher correlations with human judgments compared to existing metrics, such as \textsc{G-Eval} and \textsc{GPTScore}, underscoring its potential as a more reliable and effective evaluation framework for natural language generation tasks. The code for our experiments is available at \url{https://anonymous.4open.science/r/check-eval-0DB4}

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