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StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs
arXiv – CS AI|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|
🤖AI Summary
Researchers introduce StructEval, a comprehensive benchmark for evaluating Large Language Models' ability to generate structured outputs across 18 formats including JSON, HTML, and React. Even state-of-the-art models like o1-mini only achieve 75.58% average scores, with open-source models performing approximately 10 points lower.
Key Takeaways
- →StructEval benchmark tests LLMs on both non-renderable formats (JSON, YAML, CSV) and renderable formats (HTML, React, SVG).
- →The benchmark includes 44 different task types across generation and conversion paradigms.
- →Top-performing model o1-mini only achieves 75.58% average score, showing significant room for improvement.
- →Open-source models lag behind proprietary models by approximately 10 percentage points.
- →Generation tasks prove more challenging than conversion tasks, with visual content being particularly difficult.
#llm#benchmark#structured-output#ai-evaluation#json#html#react#performance-testing#open-source#code-generation
Read Original →via arXiv – CS AI
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