{
 "id": "knowledge-verse-ai/TeXpert",
 "owner": "knowledge-verse-ai",
 "name": "TeXpert",
 "downloads": 62,
 "likes": 0,
 "last_modified": "2025-09-14T17:32:06.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
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 "tasks": [
  "text-generation"
 ],
 "citation": "@inproceedings{kale-nadadur-2025-texpert,\n    title = \"{T}e{X}pert: A Multi-Level Benchmark for Evaluating LaTeX Code Generation by {LLM}s\",\n    author = \"Kale, Sahil  and\n      Nadadur, Vijaykant\",\n    editor = \"Ghosal, Tirthankar  and\n      Mayr, Philipp  and\n      Singh, Amanpreet  and\n      Naik, Aakanksha  and\n      Rehm, Georg  and\n      Freitag, Dayne  and\n      Li, Dan  and\n      Schimmler, Sonja  and\n      De Waard, Anita\",\n    booktitle = \"Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.sdp-1.2/\",\n    doi = \"10.18653/v1/2025.sdp-1.2\",\n    pages = \"7--16\",\n    ISBN = \"979-8-89176-265-7\",\n    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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 "reason": "The declared licence (mit) is classified as permissive.",
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}