README.md

---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|openai_humaneval
- extended|mbpp
task_categories: []
task_ids: []
pretty_name: MultiPLE-E
tags: []
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---

# Dataset Card for MultiPL-E

## Dataset Description

- **Homepage:**  https://nuprl.github.io/MultiPL-E/
- **Repository:**  https://github.com/nuprl/MultiPL-E
- **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177
- **Point of Contact:** carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu

## Dataset Summary

MultiPL-E is a dataset for evaluating large language models for code
generation that supports 22 programming languages. It takes the OpenAI 
HumanEval and the Mostly Basic Python Programs (MBPP) benchmarks and uses little compilers to
translate them  to other languages. It is easy to add support for new languages 
and benchmarks.

The dataset is divided into several configurations named *SRCDATA-LANG*, where
*SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported
languages. We use the canonical file extension for each language to identify
the language, e.g., "cpp" for C++, "lua" for Lua, "clj" for Clojure, and so on.

## Using MultiPL-E

- MultiPL-E is part of the [BigCode Code Generation LM Harness]. This
  is the easiest way to use MultiPL-E.

- MultiPL-E has its own evaluation framework that supports proprietary models,
  the prompt ablations, more source benchmarks, and more recently added
  programming languages. See the [MultiPL-E tutorial] on how to use this
  framework directly.

## The MultiPL-E Ablations

The MultiPL-E paper presented several ablations of the prompt for the original
set of programming languages. We do not include them in the current version of
MultiPL-E, but they are still available in this repository from revision
`d23b094` or earlier. (You can optionally pass the revision to
`datasets.load_dataset`.)

These are the prompt variations:

- *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt
  is totally unchanged. If the original prompt had Python doctests, they remain
  as Python instead of being translated to *LANG*. If the original prompt had 
  Python-specific terminology, e.g., "list", it remains "list", instead of 
  being translated, e.g., to "vector" for C++.

- *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves
  the natural language text of the prompt unchanged.

- *SRCDATA-LANG-removed* removes the doctests from the prompt.

Note that MBPP does not have any doctests, so the "removed" and "transform"
variations are not available for MBPP.

## Changelog

### Version 3.0

This is the first significant update since MultiPL-E was used in StarCoder 1.

1. We no longer publish the MultiPL-E ablations, but they are available in
   revision `d23b094` and earlier.
2. New programming languages supported:
   - Clojure, thanks to [Alex Miller](https://github.com/puredanger)
   - Elixir, thanks to [Marko Vukovic](https://github.com/mvkvc)
   - Haskell, thanks to https://github.com/Cajunvoodoo
   - OCaml, thanks to [John Gouwar](https://johngouwar.github.io)
3. Changes to existing HumanEval-based problems:
   - Four Scala problems have fixed prompts/tests (12, 90, 128, 162).
   - Some whitespace-only changes to problems for Racket (18 problems),
     R (36 problems), Julia (159 problems), and D (156 problems). We will try to
     avoid these kinds of changes in the future.
1. The MBPP-based problems have changes analogous to the HumanEval-based problems.

See the directory `diffs_v3.0` in the dataset repository for the diffs to
each prompt.

[BigCode Code Generation LM Harness]: https://github.com/bigcode-project/bigcode-evaluation-harness
[MultiPL-E tutorial]: https://nuprl.github.io/MultiPL-E/