Summary of Scicode: a Research Coding Benchmark Curated by Scientists, By Minyang Tian et al.

SciCode: A Research Coding Benchmark Curated by Scientists

by Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, Xinan Chen, Cunwei Fan, Xuefei Guo, Roland Haas, Pan Ji, Kittithat Krongchon, Yao Li, Shengyan Liu, Di Luo, Yutao Ma, Hao Tong, Kha Trinh, Chenyu Tian, Zihan Wang, Bohao Wu, Yanyu Xiong, Shengzhu Yin, Minhui Zhu, Kilian Lieret, Yanxin Lu, Genglin Liu, Yufeng Du, Tianhua Tao, Ofir Press, Jamie Callan, Eliu Huerta, Hao Peng

First submitted to arxiv on: 18 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This research paper tackles the challenge of evaluating language models (LMs) by creating a benchmark called SciCode, which consists of 338 subproblems decomposed from 80 challenging main problems. These subproblems require LMs to demonstrate knowledge recall, reasoning, and code synthesis skills in various natural science fields, including mathematics, physics, chemistry, biology, and materials science. The paper involves input from scientists and AI researchers in these domains, providing a scientist-curated coding benchmark for evaluating the capabilities of language models like Claude3.5-Sonnet, which can solve only 4.6% of the problems in the most realistic setting.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Imagine if computers could help scientists with real-world research problems! This paper creates a special test called SciCode to see how well computer programs can do just that. They made it by asking experts in different science fields like math, physics, and biology what kind of coding challenges would be most helpful for them. The result is 338 small puzzles that require computers to recall scientific knowledge, think critically, and write code. The best computer program tested can solve only a tiny fraction (4.6%) of these puzzles correctly. This research shows how far we have come in developing AI that can assist scientists and highlights the importance of creating better evaluation tools for future advancements.