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Paul Gauthier 2023-07-01 22:03:08 -07:00
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docs/benchmarks.md
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@ -105,7 +105,7 @@ Only use standard python libraries, don't suggest installing any packages.
Aider updates the implementation file based on GPT's reply and runs
the unit tests. If all tests pass, the exercise is considered
complete. If some tests fail, Aider sends GPT a second message with
complete. If some tests fail, aider sends GPT a second message with
the test error output. It only sends the first 50 lines of test errors
to try and avoid exceeding the context window of the smaller models. Aider
also includes this final instruction:
@ -119,7 +119,7 @@ Fix the code in <implementation file> to resolve the errors.
Requiring GPT to fix its first implementation in response to test failures
is another way in which this benchmark stresses code editing skill.
This second chance is also important because it
gives GPT a chance to adjust if the
gives GPT the opportunity to adjust if the
instructions were imprecise with respect to the
specific requirements of the unit tests.
Many of the exercises have multiple paragraphs of instructions,
@ -135,14 +135,14 @@ the intermediate performance after the first coding attempt,
without the benefit of the second try.
It's worth noting that GPT never gets to see the source code of the
unit tests during the benchmarking. It only sees the error output from
unit tests during the benchmark. It only sees the error output from
failed tests. Of course, all of this code was probably part of its
original training data!
In summary, passing an exercise means GPT was able to:
- Write the required code (possibly after reviewing test error output),
- Correctly package all of the code edits into the edit format so that Aider can process and save it to the implementation file.
- Correctly package all of the code edits into the edit format so that aider can process and save it to the implementation file.
Conversely, failing an exercise only requires a breakdown in one of
those steps. In practice, GPT fails at different steps in different