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Multi-Programming Language Evaluation of Large Language Models of Code (MultiPL-E)

MultiPL-E is a system for translating unit test-driven neural code generation benchmarks to new languages. We have used MultiPL-E to translate two popular Python benchmarks (HumanEval and MBPP) to 18 other programming languages.

For more information:

• MultiPL-E is part of the BigCode Code Generation LM Harness. This is the easiest way to use MultiPL-E.
• The Multilingual Code Models Evaluation by BigCode evaluates Code LLMs using several benchmarks, including MultiPL-E.
• We have a tutorial on how to use MultiPL-E directly.
• Read our paper MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation.
• The MultiPL-E dataset of translated prompts is available on the Hugging Face Hub.

Versions

• Version 3.0

  • We are going to maintain the changelog on the dataset page: https://huggingface.co/datasets/nuprl/MultiPL-E
  • The dataset was versioned at 3.0, and we are bumping the software version to stay in sync.
  • We have published several new PLs in the dataset. However, we have not included these PLs at this time: Dafny, Coq, Lean, Luau, and MATLAB.

• Version 0.5.0: Instruction-following support and new languages

  • New languages: Luau, Elixir, Lean, Coq, Dafny
  • Support for instruction-following prompts
  • vLLM support for faster evaluation

• Version 0.4.0: QoL improvements and new languages

  • New languages: OCaml, MATLAB
  • Using .jsonl instead of .json for prompts
  • Several bugfixes to prompts

• Version 0.3.0: used to evaluate StarCoder

  • This version corrects several bugs in prompts and test cases that resulted in lower pass@k rates for some of the statically typed languages. The most significant difference is that the pass@k for Java increases by about 2% on HumanEval.

• Version 0.2.0: used to evaluate SantaCoder