StructEval: Benchmarking LLMs' Capabilities to Generate Structural
  Outputs
Authors: name: Jialin Yang, name: Dongfu Jiang, name: Lipeng He, name: Sherman Siu, name: Yuxuan Zhang, name: Disen Liao, name: Zhuofeng Li, name: Huaye Zeng, name: Yiming Jia, name: Haozhe Wang, name: Benjamin Schneider, name: Chi Ruan, name: Wentao Ma, name: Zhiheng Lyu, name: Yifei Wang, name: Yi Lu, name: Quy Duc Do, name: Ziyan Jiang, name: Ping Nie, name: Wenhu Chen

As Large Language Models (LLMs) become integral to software development
workflows, their ability to generate structured outputs has become critically
important. We introduce StructEval, a comprehensive benchmark for evaluating
LLMs' capabilities in producing both non-renderable (JSON, YAML, CSV) and
renderable (HTML, React, SVG) structured formats. Unlike prior benchmarks,
StructEval systematically evaluates structural fidelity across diverse formats
through two paradigms: 1) generation tasks, producing structured output from
natural language prompts, and 2) conversion tasks, translating between
structured formats. Our benchmark encompasses 18 formats and 44 types of task,
with novel metrics for format adherence and structural correctness. Results
reveal significant performance gaps, even state-of-the-art models like o1-mini
achieve only 75.58 average score, with open-source alternatives lagging
approximately 10 points behind. We find generation tasks more challenging than
conversion tasks, and producing correct visual content more difficult than
generating text-only structures.