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--- |
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dataset_info: |
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features: |
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- name: benchmark |
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dtype: string |
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- name: artifact_type |
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dtype: string |
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- name: problem_id |
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dtype: string |
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- name: test_id |
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dtype: string |
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- name: variables |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1889380696 |
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num_examples: 1082 |
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download_size: 820277839 |
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dataset_size: 1889380696 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: mit |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: SciCode |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for Dataset Name |
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Official Description (from the authors): |
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Since language models (LMs) now outperform average humans on many challenging tasks, |
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it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. |
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We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. |
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Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, |
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including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, |
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SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, |
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and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. |
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It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions |
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and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, |
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can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' |
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progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future. |
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This repository contains the ground truth artifacts that's needed for LightEval benchmarks. |
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The original SciCode numerical evaluation artifacts are provided in |
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`raw/raw_ground.h5` for reproducibility and parity with the original |
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SciCode evaluation pipeline. |
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This dataset uses a single split (`train`) as it represents a complete |
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set of SciCode numerical evaluation artifacts rather than training data. |
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## Dataset Details |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** [https://github.com/scicode-bench/SciCode?tab=readme-ov-file] |
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- **Paper [optional]:** [https://arxiv.org/abs/2407.13168] |
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## Dataset Card Authors |
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The original authors of SciCode benchmark and Akshath Mangudi for |
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providing the ground truth artifact. |
