AutoCodeBench: Large Language Models are Automatic Code Benchmark
  Generators
Authors: name: Jason Chou, name: Ao Liu, name: Yuchi Deng, name: Zhiying Zeng, name: Tao Zhang, name: Haotian Zhu, name: Jianwei Cai, name: Yue Mao, name: Chenchen Zhang, name: Lingyun Tan, name: Ziyan Xu, name: Bohui Zhai, name: Hengyi Liu, name: Speed Zhu, name: Wiggin Zhou, name: Fengzong Lian

Large Language Models (LLMs) have demonstrated remarkable capabilities across
various domains, with code generation emerging as a key area of focus. While
numerous benchmarks have been proposed to evaluate their code generation
abilities, these benchmarks face several critical limitations. First, they
often rely on manual annotations, which are time-consuming and difficult to
scale across different programming languages and problem complexities. Second,
most existing benchmarks focus primarily on Python, while the few multilingual
benchmarks suffer from limited difficulty and uneven language distribution. To
address these challenges, we propose AutoCodeGen, an automated method for
generating high-difficulty multilingual code generation datasets without manual
annotations. AutoCodeGen ensures the correctness and completeness of test cases
by generating test inputs with LLMs and obtaining test outputs through a
multilingual sandbox, while achieving high data quality through reverse-order
problem generation and multiple filtering steps. Using this novel method, we
introduce AutoCodeBench, a large-scale code generation benchmark comprising
3,920 problems evenly distributed across 20 programming languages. It is
specifically designed to evaluate LLMs on challenging, diverse, and practical
multilingual tasks. We evaluate over 30 leading open-source and proprietary
LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The
results show that even the most advanced LLMs struggle with the complexity,
diversity, and multilingual nature of these tasks. Besides, we introduce
AutoCodeBench-Complete, specifically designed for base models to assess their
few-shot code generation capabilities. We hope the AutoCodeBench series will
serve as a valuable resource and inspire the community to focus on more
challenging and practical multilingual code generation scenarios.