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arxiv:2407.13168

SciCode: A Research Coding Benchmark Curated by Scientists

Published on Jul 18
· Submitted by amber1120 on Jul 22
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Abstract

Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.

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Hi @amber1120 congrats on this work!

Are you planning to share the dataset on the hub? Here's a guide: https://huggingface.co/docs/datasets/loading.

The dataset could then be loaded in 2 lines of code, like so:

from datasets import load_dataset

dataset  = load_dataset("your-hf-organization/scicode")

It could then also be linked to this paper, as explained here: https://huggingface.co/docs/hub/en/datasets-cards#linking-a-paper.

We could also set up a Space using Gradio for the leaderboard.

Let me know if you need any help!

Cheers,
Niels
Open-source @ HF

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