StructEval: Benchmarking LLMs’ Capabilities to Generate Structural Outputs
Summary: arXiv:2505.20139v3 Announce Type: replace-cross
As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. In response to this need, we introduce StructEval, a comprehensive benchmark designed to evaluate the capabilities of LLMs in producing both non-renderable (such as JSON, YAML, and CSV) and renderable (including HTML, React, and SVG) structured formats.
Overview of StructEval
StructEval distinguishes itself from prior benchmarks by systematically assessing structural fidelity across a diverse range of formats. The benchmark operates through two primary paradigms:
- Generation Tasks: These tasks involve producing structured output directly from natural language prompts.
- Conversion Tasks: These tasks focus on translating between various structured formats.
Comprehensive Format Coverage
StructEval encompasses an impressive array of 18 different formats and 44 types of tasks. This extensive scope ensures a thorough evaluation of LLM capabilities. Additionally, the benchmark introduces novel metrics aimed at measuring format adherence and structural correctness, which are crucial for assessing the quality of generated outputs.
Key Findings
Initial results from the StructEval benchmark reveal significant performance gaps among the evaluated models. Even state-of-the-art models, such as o1-mini, achieve only an average score of 75.58, indicating room for improvement. Notably, open-source alternatives tend to lag approximately 10 points behind their proprietary counterparts.
Challenges in LLM Performance
The findings from StructEval also highlight the differing levels of difficulty associated with various tasks. Specifically, generation tasks are found to be more challenging than conversion tasks. Furthermore, the benchmark indicates that producing correct visual content is significantly more difficult than generating text-only structures.
Conclusion
As LLMs continue to evolve and play a pivotal role in software development, understanding their strengths and weaknesses in generating structured outputs is paramount. StructEval offers a necessary framework for evaluating these capabilities, paving the way for future advancements in LLM technology. By addressing the identified performance gaps, developers and researchers can work towards creating more robust models that excel in generating both non-renderable and renderable structured formats.
Future Directions
Moving forward, StructEval aims to refine its methodologies and expand its scope to include even more formats and task types. This ongoing effort will not only enhance the benchmark’s effectiveness but also contribute to the broader understanding of LLM capabilities in structured output generation.