Are LLMs good at structured outputs? A benchmark for evaluating structured output capabilities in LLMs

Abstract

Existing benchmarks for Large Language Models (LLMs) mostly focus on general or specific domain capabilities, overlooking structured output capabilities. We introduce SoEval, a benchmark for assessing LLMs’ ability to generate structured outputs like JSON, XML, and lists. SoEval contains 3.7K entries in Chinese and English, covering 13 types of structured output tasks across 20 subjects. In experiments, we found that while current mainstream LLMs have deficiencies in structured output, GPT-4 outperforms them in this aspect. GPT-4 achieved an average score of 0.4 on SoEval, representing a 24% enhancement over the next best-performing model. At the same time, the performance of current mainstream models on English tasks is also better than on Chinese tasks. We also report the performance of mainstream large models on different structured output types and task subjects. The benchmark construction code and SoEval dataset are open-sourced at https://github.com/MoranCoder95/SoEval.