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release this data along with an evaluation framework at
https://www.github.com/openai/human-eval.
To solve a problem in our test set, we generate multiple
samples from the models, and check if any of them pass the
unit tests. With just a single sample, a 12B parameter Codex
solves 28.8% of these problems, and a 300M parameter
Codex solves 13.2% of these problems. In contrast, the 6B
parameter GPT-J (Wang & Komatsuzaki, 2021) achieves
11.4% on the same dataset, while all GPT models achieve
near 0%. To improve our model’s performance at the task of
function synthesis from docstrings, we fine-tune Codex on
standalone, correctly implemented functions. The resulting
model, Codex-S, solves 37.7% of problems with a single
sample. Figure 2 showcases problems of varying difficulty
in our dataset, along with correct model generated solutions.
Real-world programming tasks often involve iterations of
approaches and bug fixes, which is approximated by generating many samples from our models and selecting one that
passes all unit tests. Within 100 samples, Codex-S is able to
generate at least one correct function for 77.5% of the problems. This result suggests that accurate code samples can
be selected via heuristic ranking instead of fully evaluating
each sample, the latter of which may not be possible or practical in deployment. Indeed, we find that the sample with
highest mean log-probability passes unit tests for 44.5% of
the problems.
We conclude by discussing the limitations and potential
broader impacts of these Codex models and of increasingly
powerful code generating models more generally.
2. Evaluation Framework
In this section, we discuss the details of our evaluation
framework. We begin by defining the pass@k metric, and
explain its advantages over standard match-based metrics.
Next, we describe the dataset of hand-written problems,
called “HumanEval,” which we created in order to benchmark our models. Finally, we discuss the sandbox environment we used to safely execute model-generated code.
2.1. Functional Correctness
Generative models for code are predominantly benchmarked
by matching samples against a reference solution, where
the match can be exact or fuzzy (as in BLEU score). However, recent work has surfaced deficiencies in match-based
metrics for code. For instance, Ren et al. (2020) finds that
BLEU has problems capturing semantic features specific
to code, and suggests several semantic modifications to the
score.
More fundamentally, match-based metrics are unable to account for the large and complex space of programs functionally equivalent to a reference solution. As a consequence,
recent works in unsupervised code translation (Lachaux
et al., 2020) and pseudocode-to-code translation (Kulal et al.,
2019) have turned to functional correctness instead, where
a sample is considered correct if it passes a set of unit tests.
We argue that this metric should be applied to docstringconditional code generation as well.
Perhaps the most convincing reason to evaluate functional
correctness is that it is used by human developers to judge
code. A framework known as test-driven development dictates that software requirements be converted into test cases
before any implementation begins, and success is defined
by a program that passes these tests. While few organizations employ full test-driven development, integration of
new code is usually dependent on creating and passing unit
tests.
Kulal et al. (2019) evaluate functional correctness using
the pass@k metric, where k code samples are generated
per problem, a problem is considered solved if any sample
Evaluating Large Language Models Trained on Code
Figure 2. Three example problems from the HumanEval dataset, where the probabilities that a single sample from Codex-12B passes unit
tests are 0.9, 0.17, and 0.005. The prompt provided to the model is shown with a white background, and a successful model-generated
completion is shown in a yellow background. Though not a guarantee for problem novelty, all problems were hand-written and not
programmatically copied from existing sources. Random problems and samples can be found in Appendix B.
passes the unit tests, and the total fraction of problems
solved is reported. However, computing pass@k in this
way can have high variance. Instead, to evaluate pass@k,
we generate n ≥ k samples per task (in this paper, we
use n = 200 and k ≤ 100), count the number of correct
samples c ≤ n which pass unit tests, and calculate the
unbiased estimator
Oh, look who's finally waking up
You look pale, but that's to be expected
I did pump you fall of all sorts drugs after all
I can't imagine you'll be very coherent for a while yet
What was that? Speak up Cutie
I can barely hear you through those little squeak
Where are you? Here at my home more accurately in my basement and this little cage I set up for you
It's not the best setup up, that I'm still working on a more permanent location for you upstairs
So sorry, but you'll have to put up with this cold damper for a while longer
How are you feeling? Aside from now obvious? Leah, possible nausea, and headache
Alright
Of course, you're feeling sick
I'll get you some medicine for that later
So long as you behave
You don't remember what happened, do you? Can tell by the confused look on your face
He came into the cafe at work yet
I made Latte
You were mop around
Said you didn't have a date for Valentine's day
No girlfriend
No one close enough to even think about asking out I did my best to cheer you up
Put on my best cute for you and everything
Damn
Nothing I did worked
You were just miserable
You said you would done anything to get a Valentine so I helped you out
I've been a little something in your drink and your food
Oh, and that plate I accidentally broke and had you helped me cleanup was also covered in a drug
Wax wonders when it gets into your bloodstream
From that little cut you got? At first, I was a bit concerned
I used too much
But I couldn't ask it not working out