--- dataset_info: - config_name: humaneval-jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens dtype: string splits: - name: test num_bytes: 166847 num_examples: 159 download_size: 66024 dataset_size: 166847 - config_name: humaneval-lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens dtype: string splits: - name: test num_bytes: 183781 num_examples: 161 download_size: 66506 dataset_size: 183781 - config_name: humaneval-ml features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens dtype: string splits: - name: test num_bytes: 169678 num_examples: 155 download_size: 65419 dataset_size: 169678 - config_name: humaneval-r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens dtype: string splits: - name: test num_bytes: 198952 num_examples: 161 download_size: 68467 dataset_size: 198952 - config_name: humaneval-rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens dtype: string splits: - name: test num_bytes: 195422 num_examples: 161 download_size: 66881 dataset_size: 195422 configs: - config_name: humaneval-jl data_files: - split: test path: humaneval-jl/test-* - config_name: humaneval-lua data_files: - split: test path: humaneval-lua/test-* - config_name: humaneval-ml data_files: - split: test path: humaneval-ml/test-* - config_name: humaneval-r data_files: - split: test path: humaneval-r/test-* - config_name: humaneval-rkt data_files: - split: test path: humaneval-rkt/test-* --- # Dataset Card for MultiPL-E-fixed (OCaml, Lua, R, Racket, Julia) This dataset provides corrections for the **OCaml, Lua, R, Racket, and Julia** portions of the [nuprl/MultiPL-E](https://github.com/nuprl/MultiPL-E) benchmark. ### Original Dataset Information - **Repository:** https://github.com/nuprl/MultiPL-E - **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177 - **Original Point of Contact:** carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu ### This Version - **Repository:** https://github.com/jsbyun121/MultiPL-E-fixed --- ## Dataset Summary MultiPL-E is a large-scale dataset for evaluating code generation models across 22 programming languages. However, analysis of the dataset revealed several logical errors, inconsistencies, and language-specific issues in the generated prompts and test cases. These issues can lead to inaccurate evaluation scores by unfairly penalizing models for correctly identifying flaws in the prompts. This repository provides a **corrected version** of the dataset specifically for **OCaml, Lua, R, Racket, and Julia**. The goal of this version is to provide a more reliable and accurate benchmark for evaluating Large Language Models on these languages. ## Summary of Corrections The following modifications were made to address issues in the original dataset. ### 1. Logical Problems in Prompts and Test Cases Several problems in the HumanEval portion of the dataset were corrected for the following issues: * **`HumanEval_75_is_multiply_prime`**: Resolved a mismatch between problem instructions and test cases. * **`HumanEval_92_any_int`**: Fixed an incorrect test case that did not align with the problem's requirements. * **`HumanEval_116_sort_array`**: Corrected a discrepancy between the sorting criteria in the instructions and the test cases. * **`HumanEval_128_prod_signs`**: Amended an incorrect example in the prompt's docstring. * **`HumanEval_140_fix_spaces`**: Corrected a faulty test case. * **`HumanEval_142_sum_squares`**: Repaired corrupted or syntactically incorrect examples. * **`HumanEval_145_order_by_points`**: Clarified vague and ambiguous logic in the question to provide a more precise problem statement. * **`HumanEval_148_bf`**: Fixed a contradiction between the provided examples and the main instructions. * **`HumanEval_151_double_the_difference`**: Replaced an incorrect test case that produced an invalid result. * **`HumanEval_162_string_to_md5`**: Addressed incorrect handling for language-specific `None`/`null` data types required by the test cases. ### 2. General Prompt Ambiguities * **0-Based Indexing:** Added clarifications to prompts where array/list index interpretation was ambiguous, explicitly enforcing a 0-based convention to ensure consistent behavior. ### 3. Language-Specific Fixes * **R:** Corrected issues related to the handling of empty vectors, a common edge case. * **OCaml:** Fixed incorrect usage of unary operators to align with OCaml's syntax. * **Julia:** Resolved parsing issues caused by the triple-quote (`"""`) docstring character. ## Using This Dataset This corrected dataset is designed to be a **drop-in replacement** for the official MultiPL-E data for OCaml, Lua, R, Racket, and Julia. To use it, simply replace the original `humaneval-[lang]` files with the corrected versions provided in this repository. The data structure remains compatible with standard evaluation frameworks. ## Citation and Attribution If you use this corrected version of the dataset in your work, we ask that you please cite the original MultiPL-E paper and also acknowledge this repository for the corrections. **Original Paper:** ```bibtex @inproceedings{cassano2023multipl, title={MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation}, author={Cassano, Federico and Gouwar, John and Nguyen, Daniel and Nguyen, Tuan and Phothilimthana, Phitchaya and Pinckney, David and Anderson, Carolyn and Feldman, Michael and Guha, Arjun}, booktitle={2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)}, pages={707--719}, year={2023}, organization={IEEE} }