AwesomeREA.org

2025 · Preprint

StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

Jialin Yang; Dongfu Jiang; Lipeng He; Sherman Siu; Yuxuan Zhang; Disen Liao; Zhuofeng Li; Huaye Zeng; Yiming Jia; Haozhe Wang; Benjamin Schneider; Chi Ruan; Wentao Ma; Zhiheng Lyu; Yifei Wang; Yi Lu; Quy Duc Do; Ziyan Jiang; Ping Nie; Wenhu Chen

Evaluates generation and conversion of structured outputs, including HTML, React and SVG, with separate checks for format adherence and structural correctness.

Scope and limitations

Not a software reverse-engineering or GUI-navigation benchmark. Structured-output scores do not prove recovered source identity, backend reconstruction or behavioral equivalence; aggregate scores are not fully correct-output percentages.

Why it fits this collection

A supporting method for checking structured and rendered artifacts produced during frontend reconstruction; parseability alone does not establish correct content.

Publication and review details

Venue
arXiv
Status
Preprint
Publication / posting date
May 26, 2025
Date source
Original arXiv first-submission date; reviewed preprint edition retained.
First arXiv submission
May 26, 2025
arXiv
2505.20139
Reviewed arXiv version
v3

This record describes the original arXiv edition. Homepage or Scholar venue labels are not substituted for publisher evidence. Supporting method, not a demonstrated reconstruction result.

Sources reviewed

Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.

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Primary sources

  1. https://arxiv.org/abs/2505.20139v3
  2. https://github.com/TIGER-AI-Lab/StructEval
  3. https://tiger-ai-lab.github.io/StructEval/