While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. It can be difficult to accurately assess code generation performance, and there has been surprisingly little work on evaluating code generation in a way that is both flexible and rigorous. To meet this challenge, we introduce APPS, a benchmark for code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate Python code fulfilling this specification. Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. We fine-tune large language models on both GitHub and our training set, and we find that the prevalence of syntax errors is decreasing exponentially. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems, so we find that machine learning models are beginning to learn how to code. As the social significance of automatic code generation increases over the coming years, our benchmark can provide an important measure for tracking advancements.

通过引入APPs基准，对编程语言生成进展进行了评估，并发现当前机器学习模型已经开始学会编码，然而在生成Python代码时仍存在语法错误

使用APPS评估编码挑战能力