Measuring Coding Challenge Competence With APPS
Abstract:
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.
(Source: Dan Hendrycks et al.)
