{
 "body": "\nPart of the [MultiPL-E](multipl_e.md) family.\n\n## What it measures\n\nC++ is a compiled, statically typed systems language with manual memory management and templates. MultiPL-E translates the HumanEval and MBPP prompts into C++ by rewriting\nthe function signature, docstring and tests with C++'s own syntax and type system, then asks the\nmodel to complete the function body. The nuprl/MultiPL-E dataset card lists 161 HumanEval-derived items\nand 397 MBPP-derived items for C++, both a little short of the original Python pools because a\nhandful of problems could not be ported faithfully. Completions run against a real C++ toolchain\ninside a container and are checked against the translated tests; there is no partial credit for a\ncompletion that fails to compile or fails any test.\n\n## Reading the numbers\n\nA high pass@1 here shows a model can produce working C++ for short, self-contained problems whose\nlogic it likely already knows from Python; it does not test C++-specific idiom, library or ecosystem\nknowledge beyond what one function needs. Compare this score against `multipl_e_python` and other\nMultiPL-E language pages for the same model: a large gap usually reflects less C++ in the model's\ntraining data rather than a difference in reasoning ability. See the [MultiPL-E](multipl_e.md) family\npage for dataset licence, contamination and harness details shared by every language in the family.\n",
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 ],
 "page": {
  "aliases": [],
  "category": "coding",
  "contamination": {
   "note": "Inherits the source HumanEval/MBPP contamination risk (public since 2021-2022); see the multipl_e family page for detail.",
   "risk": "high"
  },
  "dataset": {
   "languages": [
    "C++"
   ],
   "license": "MIT",
   "modalities": [
    "code"
   ],
   "public_test_set": true,
   "size": null,
   "size_note": "Reading the nuprl/MultiPL-E dataset card on 2026-09-07: 161 HumanEval-derived items (of the original 164) and 397 MBPP-derived items, both a little short of the full Python originals because a handful of problems could not be ported to C++ faithfully.",
   "splits": "test (humaneval-cpp and mbpp-cpp configs)",
   "url": "https://huggingface.co/datasets/nuprl/MultiPL-E"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice C",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "bigcode-evaluation-harness task multiple-cpp"
  },
  "id": "multipl_e_cpp",
  "last_updated": "",
  "leaderboard_url": "",
  "lineage": {
   "family": "multipl_e",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "This subset translates the HumanEval and MBPP prompts into C++ by rewriting each problem's function signature, docstring and tests with C++ syntax and typing, then asks the model to complete the function body in C++. The underlying algorithmic problem is unchanged from the Python original; only the surface language differs.\n",
  "metric": {
   "baseline_note": "No published human baseline.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "pass@1",
   "random_baseline": 0,
   "unit": "%"
  },
  "name": "MultiPL-E: C++",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2208.08227",
   "title": "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation",
   "url": "https://arxiv.org/abs/2208.08227",
   "year": 2022
  },
  "publisher": {
   "authors": [
    "Federico Cassano",
    "John Gouwar",
    "Daniel Nguyen",
    "Sydney Nguyen",
    "Luna Phipps-Costin",
    "Donald Pinckney",
    "Ming-Ho Yee",
    "Yangtian Zi",
    "Carolyn Jane Anderson",
    "Molly Q Feldman",
    "Arjun Guha",
    "Michael Greenberg",
    "Abhinav Jangda"
   ],
   "org": "Northeastern University Programming Research Lab (nuprl)",
   "url": "https://github.com/nuprl/MultiPL-E"
  },
  "released": "2022-08",
  "repo_url": "https://github.com/nuprl/MultiPL-E",
  "saturation": {
   "as_of": "",
   "note": "Not established at the per-language level; see the multipl_e family page for the general pattern of high-resource versus low-resource language spread.",
   "status": "unknown",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation",
    "url": "https://arxiv.org/abs/2208.08227"
   },
   {
    "accessed": "2026-09-07",
    "title": "nuprl/MultiPL-E dataset card",
    "url": "https://huggingface.co/datasets/nuprl/MultiPL-E"
   },
   {
    "accessed": "2026-09-07",
    "title": "nuprl/MultiPL-E repository",
    "url": "https://github.com/nuprl/MultiPL-E"
   }
  ],
  "status": "active",
  "subcategory": "multilingual code generation",
  "summary": "The C++ subset of MultiPL-E: HumanEval and MBPP function-completion problems translated into C++ and scored with pass@1.",
  "tags": [
   "code-generation",
   "cpp",
   "pass-at-k",
   "humaneval",
   "mbpp"
  ],
  "task_format": "Function completion in C++: given a translated signature, docstring and (for HumanEval-derived items) doctests, the model generates a function body, which is compiled or interpreted with a real C++ toolchain inside a container and checked against translated unit tests.\n"
 }
}