# TeXBLEU: Automatic Metric for Evaluate LaTeX Format

arXiv: https://arxiv.org/abs/2409.06639 · HTML: https://mlsanity.com/paper/2409.06639/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [The Impact of Positional Encoding on Length Generalization in Transformers](https://mlsanity.com/paper/2305.19466/) (2023)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)
- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://mlsanity.com/paper/2004.03720/) (2020)
- [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://mlsanity.com/paper/2006.06264/) (2020)
- [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019)
- [A Simple and Effective Positional Encoding for Transformers](https://mlsanity.com/paper/2104.08698/) (2021)
- [BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text](https://mlsanity.com/paper/2109.14250/) (2021)
- [MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into LaTeX Formulas for Improved Readability](https://mlsanity.com/paper/2408.07081/) (2024)
- [LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning](https://mlsanity.com/paper/2409.01145/) (2024)
- [Using artificial-intelligence tools to make LaTeX content accessible to blind readers](https://mlsanity.com/paper/2306.02480/) (2023)

## Built on by (most cited first, 3 of 3)

- [Intelligibility of Text-to-Speech Systems for Mathematical Expressions](https://mlsanity.com/paper/2506.11086/) (2025)
- [Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences](https://mlsanity.com/paper/2508.03542/) (2025)
- [Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models](https://mlsanity.com/paper/2603.25750/) (2026)

## Abstract (arXiv)

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.