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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.