A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917801923444736 |
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| author | Soh, Yun Joon Zhao, Jishen |
| author_facet | Soh, Yun Joon Zhao, Jishen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics Soh, Yun Joon Zhao, Jishen Computation and Language Artificial Intelligence 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. |
| title | A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.10030 |