A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics

• URL: http://arxiv.org/abs/2410.10030v1
• Date: Sun, 13 Oct 2024 22:10:42 GMT
• Title: A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics
• Authors: Yun Joon Soh, Jishen Zhao,
• Abstract summary: We study the statistics of the existing evaluation metrics for a better understanding of their limitations. As a potential solution, we discuss how a Mixture Of Grader could potentially improve the auto QA evaluator quality.
• Score: 6.571049277167304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The explosion of open-sourced models and Question-Answering (QA) datasets emphasizes the importance of automated QA evaluation. We studied the statistics of the existing evaluation metrics for a better understanding of their limitations. By measuring the correlation coefficients of each evaluation metric concerning human-like evaluation score, we observed the following: (1) existing metrics have a high correlation among them concerning the question type (e.g., single word, single phrase, etc.), (2) no single metric can adequately estimate the human-like evaluation. As a potential solution, we discuss how a Mixture Of Grader could potentially improve the auto QA evaluator quality.

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