Summary of Da-code: Agent Data Science Code Generation Benchmark For Large Language Models, by Yiming Huang et al.
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
by Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces DA-Code, a code generation benchmark designed to evaluate Large Language Models (LLMs) on agent-based data science tasks. The benchmark features three core elements: challenging tasks that require advanced coding skills, diverse and complex real-world data examples, and intricate data processing using programming languages. The authors set up the benchmark in a controllable environment, aligning with real-world scenarios, and develop the DA-Agent baseline. Despite the baseline’s performance, experiments show that current best LLMs achieve only 30.5% accuracy, leaving room for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new code generation benchmark called DA-Code. This benchmark is special because it tests how well Large Language Models (LLMs) can do complex data science tasks. The tasks are hard and require the models to understand what’s going on in real-world data. The authors also use real-world examples, not fake ones. To solve these tasks, the models have to be able to write code using programming languages. They set up the benchmark so it’s like a real-world scenario and make it easy to test. They even created a baseline model that does okay but can still be improved. |
