为评估LaTeX数学表达式提出新指标TeXBLEU，显著优于传统方法。
TeXBLEU: Automatic Metric for Evaluate LaTeX Format
- 基于n-gram的BLEU思想，用预训练分词器和位置编码嵌入模型计算相似度。
- 在1000条数据的MathBridge上，比BLEU、sacreBLEU、Rouge分别提升86%、121%、610%。
- 适合需要精确评估数学公式生成质量的研究者，尤其在科学写作领域。
LaTeX广泛用于科技、数学和计算机科学领域的专业文档排版。尽管语言模型生成的LaTeX数学表达式日益增多，但缺乏有效的评估指标。本文提出TeXBLEU，一种基于n-gram的BLEU思想、专为LaTeX数学表达式设计的自动评估指标。该方法采用在arXiv论文数据集上预训练的分词器，并结合带有位置编码的微调嵌入模型，通过n-gram token相似度替代传统BLEU的精度评分。在包含1000个样本的MathBridge数据集上，TeXBLEU相较于BLEU、sacreBLEU、Rouge分别提升了86%、121%和610%。代码已开源：https://github.com/KyuDan1/TeXBLEU。
原文摘要 · Abstract (English)
LaTeX is suitable for creating specially formatted documents in science, technology, mathematics, and computer science. Although the use of mathematical expressions in LaTeX format along with language models is increasing, there are no proper evaluation matrices to evaluate them. In this study, we propose TeXBLEU, a metric for evaluating mathematical expressions in the LaTeX format built on the n-gram-based BLEU metric widely used in translation tasks. The proposed TeXBLEU consists of a predefined tokenizer trained on the arXiv paper dataset and a fine-tuned embedding model with positional encoding. The TeXBLEU score was calculated by replacing BLUE's modified precision score with the similarity of n-gram-based tokens. TeXBLEU showed improvements of 86\%, 121\%, and 610\% over traditional evaluation metrics, such as BLEU, sacreBLEU, and Rouge, respectively, on the MathBridge dataset with 1,000 data points. The code is available at https://github.com/KyuDan1/TeXBLEU.
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