2024-12-30
arXiv

HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation

Zhaojian Yu , Yilun Zhao , Arman Cohan , Xiao-Ping Zhang
The paper introduces self-invoking code generation, a new task to evaluate LLMs' progressive reasoning and problem-solving capabilities. It proposes three new benchmarks (HumanEval Pro, MBPP Pro, and BigCodeBench-Lite Pro) and finds that while most LLMs perform well on traditional benchmarks, their performance drops significantly on self-invoking tasks. The study also identifies failure modes in the evaluation results, highlighting the need for further research in this area.
We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented with a base problem and a related, more complex problem. They must solve the base problem and then utilize its solution to address the more complex one. This work features three key contributions. First, we propose a general recipe for generating more challenging versions of existing benchmarks, resulting in three new benchmarks: HumanEval Pro, MBPP Pro, and BigCodeBench-Lite Pro, specifically designed to assess LLMs on self-invoking code generation. Second, from the analysis of experimental results over twenty LLMs on our benchmarks, we have two important observations: (i) Most LLMs excel in traditional code generation benchmarks like HumanEval and MBPP, but their performance declines on self-invoking tasks. For example, o1-mini achieves 96.2% pass@1 on HumanEval but only 76.2% on HumanEval Pro. (ii) On self-invoking code generation task, the instruction-tuned models demonstrate only marginal improvements compared to the base models. Third, we disclose the types of failure modes that exist in our evaluation results. All these results underscore the need for further advancements in self-invoking code generation tasks and provide a new direction for future research on enhancing LLMs' code reasoning capabilities.