Summary of Benchmarking Chatgpt on Algorithmic Reasoning, by Sean Mcleish et al.
Benchmarking ChatGPT on Algorithmic Reasoning
by Sean McLeish, Avi Schwarzschild, Tom Goldstein
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 evaluates the ability of ChatGPT to solve algorithm problems from the CLRS benchmark suite, designed for Graph Neural Networks (GNNs). The results show that ChatGPT outperforms specialist GNN models in solving these problems using Python. This raises new points of discussion about learning algorithms with neural networks and the concept of out-of-distribution testing using web-scale training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT is a powerful tool that can solve complex algorithm problems from the CLRS benchmark suite, designed for Graph Neural Networks (GNNs). The results show that ChatGPT does better than special GNN models in solving these problems. This means we need to think about how we learn algorithms with neural networks and what it looks like when data is not normal. |
Keywords
* Artificial intelligence * Gnn
