The article outlines the evaluation procedure for code LLMs, focusing on the use of unit tests across different benchmarks. Specifically, it emphasizes the translation of assertions from MCEVAL to LuaUnit to standardize evaluations. Key metrics include pass@1, which assesses the correctness of model outputs on the first attempt, and the differentiation between various types of errors. Furthermore, inference time is measured to evaluate the generation duration, highlighting that while quantization isn't aimed at speed, it may impact performance metrics.
To streamline and standardize the automated evaluation procedure, we translated the native assertions in MCEVAL to LuaUnit-based assertions, improving consistency across benchmarks.
The evaluation involves measuring pass@1 to determine if the model produced a correct solution on its first attempt, providing a clear metric for performance comparison.
Differentiating between failed unit tests, runtime errors, and syntax errors offers valuable insights into the challenges faced when generating code with LLMs.
Inference time, which measures the duration from prompt to output generation, serves as an important metric alongside pass rates to evaluate model efficiency.
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