README.md

metadata

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: []
dataset_info:
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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
   • Elixir, thanks to Marko Vukovic
   • Haskell, thanks to https://github.com/Cajunvoodoo
   • OCaml, thanks to John Gouwar
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.
4. 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.