Summary of Spider2-v: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?, by Ruisheng Cao et al.
Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?
by Ruisheng Cao, Fangyu Lei, Haoyuan Wu, Jixuan Chen, Yeqiao Fu, Hongcheng Gao, Xinzhuang Xiong, Hanchong Zhang, Yuchen Mao, Wenjing Hu, Tianbao Xie, Hongshen Xu, Danyang Zhang, Sida Wang, Ruoxi Sun, Pengcheng Yin, Caiming Xiong, Ansong Ni, Qian Liu, Victor Zhong, Lu Chen, Kai Yu, Tao Yu
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The abstract introduces Spider2-V, a multimodal agent benchmark that evaluates the ability of vision language models (VLMs) to automate professional data science and engineering workflows. The benchmark features 494 real-world tasks in authentic computer environments, simulating tasks such as writing SQL queries, generating Python code, and managing GUI operations. Existing state-of-the-art LLM/VLM-based agents struggle to reliably automate these full data workflows, achieving only 14% success. Even with step-by-step guidance, the agents underperform in tasks requiring fine-grained GUI actions (16.2%) and remote cloud-hosted workspaces (10.6%). The study aims to pave the way for autonomous multimodal agents to transform the automation of data science and engineering workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spider2-V is a new benchmark that tests how well computer models can help people with data science and engineering tasks. Right now, these models are not very good at doing this on their own, but they’re getting better all the time. This benchmark has 494 real-world tasks that show what these models need to be able to do. The goal is to make it possible for computers to automate many of these tasks, making data science and engineering easier and more accessible. |
