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knowledge-verse-ai/TeXpert

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Permissive or share-alike

The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 756631d
Licence files: No licence file at the top level of the repositoryEvidence: file list at 756631d
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

text-generation

Citation text from the card

@inproceedings{kale-nadadur-2025-texpert,
    title = "{T}e{X}pert: A Multi-Level Benchmark for Evaluating LaTeX Code Generation by {LLM}s",
    author = "Kale, Sahil  and
      Nadadur, Vijaykant",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.sdp-1.2/",
    doi = "10.18653/v1/2025.sdp-1.2",
    pages = "7--16",
    ISBN = "979-8-89176-265-7",
    abstract = "LaTeX{'}s precision and flexibility in typesetting have made it the gold standard for the preparation of scientific documentation. Large Language Models (LLMs) present a promising opportunity for researchers to produce publication-ready material using LaTeX with natural language instructions, yet current benchmarks completely lack evaluation of this ability. By introducing TeXpert, our benchmark dataset with natural language prompts for generating LaTeX code focused on components of scientific documents across multiple difficulty levels, we conduct an in-depth analysis of LLM performance in this regard and identify frequent error types. Our evaluation across open and c

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dataset_licence({ "id": "knowledge-verse-ai/TeXpert" })
signal: "permissive"
stated_licence: "mit"
sources: 0