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LaTeX Compilation: Challenges in the Era of LLMs

arXiv:2603.02873v1 Announce Type: new Abstract: As large language models (LLMs) increasingly assist scientific writing, limitations and the significant token cost of TeX become more and more visible. This paper analyzes TeX's fundamental defects in compilation and user experience design to illustrate its limitations on compilation efficiency, generated semantics, error localization, and tool ecosystem in the era of LLMs. As an alternative, Mogan STEM, a WYSIWYG structured editor, is introduced. Mogan outperforms TeX in the above aspects by its efficient data structure, fast rendering, and on-demand plugin loading. Extensive experiments are conducted to verify the benefits on compilation/rendering time and performance in LLM tasks. What's more, we show that due to Mogan's lower information entropy, it is more efficient to use .tmu (the document format of Mogan) to fine-tune LLMs than TeX. Therefore, we launch an appeal for larger experiments on LLM training using the .tmu format.

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Tianyou Liu, Ziqiang Li, Yansong Li, Xurui Liu · · 1 min read · 5 views
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arXiv:2603.02873v1 Announce Type: new Abstract: As large language models (LLMs) increasingly assist scientific writing, limitations and the significant token cost of TeX become more and more visible. This paper analyzes TeX's fundamental defects in compilation and user experience design to illustrate its limitations on compilation efficiency, generated semantics, error localization, and tool ecosystem in the era of LLMs. As an alternative, Mogan STEM, a WYSIWYG structured editor, is introduced. Mogan outperforms TeX in the above aspects by its efficient data structure, fast rendering, and on-demand plugin loading. Extensive experiments are conducted to verify the benefits on compilation/rendering time and performance in LLM tasks. What's more, we show that due to Mogan's lower information entropy, it is more efficient to use .tmu (the document format of Mogan) to fine-tune LLMs than TeX. Therefore, we launch an appeal for larger experiments on LLM training using the .tmu format.

Executive Summary

This article critiques the LaTeX compilation process in the era of large language models (LLMs), highlighting its limitations in compilation efficiency, generated semantics, error localization, and tool ecosystem. The authors propose Mogan STEM, a WYSIWYG structured editor, as an alternative, which outperforms LaTeX in these aspects. The study demonstrates the benefits of Mogan in compilation/rendering time and performance in LLM tasks, as well as its potential for more efficient LLM training. The authors appeal for further research on LLM training using the Mogan document format (.tmu). This study has significant implications for the scientific writing community, particularly in the context of LLM-assisted writing.

Key Points

  • ▸ LaTeX compilation process has limitations in compilation efficiency, generated semantics, error localization, and tool ecosystem
  • ▸ Mogan STEM, a WYSIWYG structured editor, outperforms LaTeX in these aspects
  • ▸ Mogan demonstrates benefits in compilation/rendering time and performance in LLM tasks
  • ▸ Mogan has potential for more efficient LLM training

Merits

Strength in Addressing LaTeX Limitations

The study highlights the significant shortcomings of LaTeX compilation process in the context of LLMs, providing a clear call to action for improvement.

Comprehensive Comparison with Mogan

The authors conduct extensive experiments to demonstrate the benefits of Mogan over LaTeX, providing a thorough evaluation of the two systems.

Demerits

Limited Scope of Comparison

The study only compares Mogan with LaTeX, without considering other alternatives, which may limit the generalizability of its findings.

Lack of Standardization in LLM Training

The appeal for further research on LLM training using the Mogan document format (.tmu) highlights the need for standardization in this area, which may be challenging to achieve.

Expert Commentary

This study provides a timely critique of the LaTeX compilation process in the era of LLMs. The authors' proposal of Mogan STEM as an alternative highlights the need for more efficient and effective writing tools in the scientific community. While the study has significant merits, its limited scope of comparison and lack of standardization in LLM training are notable demerits. Nevertheless, the study's implications for the use of LLMs in scientific writing and the need for standardization in LLM training make it a valuable contribution to the field.

Recommendations

  • ✓ Future research should compare Mogan with other writing tools and formats to determine its relative effectiveness and efficiency.
  • ✓ The scientific community should prioritize standardization in LLM training and the development of a standardized format for scientific writing.

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