HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
This work addresses the need for better evaluation of LLMs' code reasoning for AI researchers, though it is incremental as it extends existing benchmarks.
The paper tackles the problem of evaluating large language models' progressive reasoning capabilities in code generation by introducing self-invoking code generation tasks, where models must solve a base problem and use its solution for a more complex one. The result shows that while models like o1-mini achieve 96.2% pass@1 on traditional benchmarks, performance drops to 76.2% on the new HumanEval Pro benchmark.
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.