Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| _version_ | 1866916830253154304 |
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| author | Laskar, Md Tahmid Rahman Islam, Mohammed Saidul Mahbub, Ridwan Masry, Ahmed Rahman, Mizanur Bhuiyan, Amran Nayeem, Mir Tafseer Joty, Shafiq Hoque, Enamul Huang, Jimmy |
| author_facet | Laskar, Md Tahmid Rahman Islam, Mohammed Saidul Mahbub, Ridwan Masry, Ahmed Rahman, Mizanur Bhuiyan, Amran Nayeem, Mir Tafseer Joty, Shafiq Hoque, Enamul Huang, Jimmy |
| contents | Charts are ubiquitous as they help people understand and reason with data. Recently, various downstream tasks, such as chart question answering, chart2text, and fact-checking, have emerged. Large Vision-Language Models (LVLMs) show promise in tackling these tasks, but their evaluation is costly and time-consuming, limiting real-world deployment. While using LVLMs as judges to assess the chart comprehension capabilities of other LVLMs could streamline evaluation processes, challenges like proprietary datasets, restricted access to powerful models, and evaluation costs hinder their adoption in industrial settings. To this end, we present a comprehensive evaluation of 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks. We design both pairwise and pointwise evaluation tasks covering criteria like factual correctness, informativeness, and relevancy. Additionally, we analyze LVLM judges based on format adherence, positional consistency, length bias, and instruction-following. We focus on cost-effective LVLMs (<10B parameters) suitable for both research and commercial use, following a standardized evaluation protocol and rubric to measure the LVLM judge's accuracy. Experimental results reveal notable variability: while some open LVLM judges achieve GPT-4-level evaluation performance (about 80% agreement with GPT-4 judgments), others struggle (below ~10% agreement). Our findings highlight that state-of-the-art open-source LVLMs can serve as cost-effective automatic evaluators for chart-related tasks, though biases such as positional preference and length bias persist. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? Laskar, Md Tahmid Rahman Islam, Mohammed Saidul Mahbub, Ridwan Masry, Ahmed Rahman, Mizanur Bhuiyan, Amran Nayeem, Mir Tafseer Joty, Shafiq Hoque, Enamul Huang, Jimmy Computation and Language Computer Vision and Pattern Recognition Charts are ubiquitous as they help people understand and reason with data. Recently, various downstream tasks, such as chart question answering, chart2text, and fact-checking, have emerged. Large Vision-Language Models (LVLMs) show promise in tackling these tasks, but their evaluation is costly and time-consuming, limiting real-world deployment. While using LVLMs as judges to assess the chart comprehension capabilities of other LVLMs could streamline evaluation processes, challenges like proprietary datasets, restricted access to powerful models, and evaluation costs hinder their adoption in industrial settings. To this end, we present a comprehensive evaluation of 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks. We design both pairwise and pointwise evaluation tasks covering criteria like factual correctness, informativeness, and relevancy. Additionally, we analyze LVLM judges based on format adherence, positional consistency, length bias, and instruction-following. We focus on cost-effective LVLMs (<10B parameters) suitable for both research and commercial use, following a standardized evaluation protocol and rubric to measure the LVLM judge's accuracy. Experimental results reveal notable variability: while some open LVLM judges achieve GPT-4-level evaluation performance (about 80% agreement with GPT-4 judgments), others struggle (below ~10% agreement). Our findings highlight that state-of-the-art open-source LVLMs can serve as cost-effective automatic evaluators for chart-related tasks, though biases such as positional preference and length bias persist. |
| title | Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.08468 |