evaluating-code-models

You can evaluate code generation models across 18 programming languages using the MultiPL-E benchmark via the BigCode Evaluation Harness, which tests functional correctness through pass@k metrics.

Yes, you can benchmark LLMs across 18 programming languages using the MultiPL-E benchmark, extending standard HumanEval evaluations to measure functional correctness in diverse language environments.

Supported benchmarks for testing functional correctness include HumanEval, MBPP, MultiPL-E, APPS, and DS-1000, covering various programming languages and complexity levels for comprehensive model evaluation.

Yes, you must install the bigcode-evaluation-harness library along with Python dependencies like transformers, accelerate, and datasets to execute the model evaluation scripts.

You can compare two code generation models by running both against standard benchmarks like HumanEval and MBPP, generating pass@k scores to objectively determine which model performs better at code generation.

Limitations include dependency on specific Python libraries like transformers and datasets, and confinement to supported benchmarks like HumanEval and MultiPL-E for functional correctness testing.