metadata
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: pbeworld
PBE World
A synthetic dataset generator for Programming-by-Examples (PBE) tasks focused on string manipulation pipelines. This is a lightly extended version of PBEBench by Naik et al. 2025.
Overview
PBE World generates tasks where models must reverse-engineer a sequence of string method calls from input/output examples. Each task consists of:
- A fixed pipeline of string operations (e.g.,
s.replace(old, new),s.upper(),s.strip(chars)) - Example input/output pairs that demonstrate the pipeline's behavior
- Hidden test cases to validate solutions
Dataset Format
Each line in the output JSONL file contains a single task with the following structure:
{
"task_id": "pbe-world/000000",
"entry_point": "apply_pipeline",
"prompt": "from typing import List\n\ndef apply_pipeline(s: str) -> str:\n \"\"\"Apply a fixed string-manipulation pipeline to the input string `s`.\n \n The pipeline consists of several string method calls such as:\n \n s = s.replace(old, new)\n s = s.upper()\n s = s.lower()\n s = s.strip(chars)\n \n which are always applied in the same fixed order.\n \n Your task is to reverse-engineer this pipeline from the examples\n and implement it.\n \n Example input/output pairs:\n \n >>> apply_pipeline('abbab') == 'aaab'\n >>> apply_pipeline('abbaa') == 'aa'\n >>> apply_pipeline('abaab') == 'baab'\n \"\"\"\n raise NotImplementedError()\n",
"canonical_solution": "def apply_pipeline(s: str) -> str:\n \"\"\"Canonical solution (auto-generated).\"\"\"\n s = s.strip('a')\n s = s.replace('bb', 'aa')\n return s\n",
"canonical_pipeline": [
{
"method": "strip",
"args": [
"a"
]
},
{
"method": "replace",
"args": [
"bb",
"aa"
]
}
],
"test": "from typing import Callable\n\ndef check(candidate: Callable[[str], str]) -> None:\n # examples from the prompt\n assert candidate('abbab') == 'aaab'\n assert candidate('abbaa') == 'aa'\n assert candidate('abaab') == 'baab'\n\n # hidden tests\n assert candidate('ababb') == 'baaa'\n assert candidate('bbbaa') == 'aab'\n assert candidate('bbbab') == 'aabab'\n",
"num_tests": 6,
"examples": [
{
"input": "abbab",
"output": "aaab"
},
{
"input": "abbaa",
"output": "aa"
},
{
"input": "abaab",
"output": "baab"
}
],
"tests": [
{
"input": "ababb",
"output": "baaa"
},
{
"input": "bbbaa",
"output": "aab"
},
{
"input": "bbbab",
"output": "aabab"
}
],
"metadata": {
"alphabet": [
"a",
"b"
],
"alphabet_size": 2,
"num_programs": 1,
"num_examples": 3,
"num_tests": 3,
"generation_params": {
"n_inputs_for_examples": 16,
"l_min": 5,
"l_max": 5,
"pipeline_L_min": 2,
"pipeline_L_max": 2
},
"split": "test",
"version": "1.0.0"
}
}
See schema.json for the complete JSON Schema specification.
Citation
If you use this dataset generator in your research, please cite:
@article{naik2024pbebench,
title={PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics},
author={Naik, Atharva and Prakam and Agrawal, Darsh and Mathur, Yash and Kapadnis, Manav and An, Yuwei and Marr, Clayton and Rose, Carolyn and Mortensen, David},
journal={arXiv preprint arXiv:2505.23126},
year={2025}
}
@software{pbeworld2025,
title={PBE World: A Synthetic Dataset Generator for String Manipulation Tasks},
author={},
year={2025},
url={https://github.com/sbdzdz/pbe-world}
}