Summary of Gpt-4 Doesn’t Know It’s Wrong: An Analysis Of Iterative Prompting For Reasoning Problems, by Kaya Stechly et al.
GPT-4 Doesn’t Know It’s Wrong: An Analysis of Iterative Prompting for Reasoning Problems
by Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
First submitted to arxiv on: 19 Oct 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This research paper investigates the reasoning abilities of Large Language Models (LLMs) in the context of Graph Coloring, a challenging NP-complete problem. The study aims to systematically evaluate the effectiveness of iterative prompting, where the model critiques its own answers or an external correct reasoner verifies proposed solutions. The results show that LLMs are poor at solving graph coloring instances and verifying candidate colorings, and that the correctness and content of criticisms do not significantly impact performance. Instead, the observed increase in effectiveness is mainly due to the correct solution being present in the top-k completions of the prompt. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well Large Language Models (LLMs) can solve a tricky problem called Graph Coloring. The researchers want to know if these models are good at figuring out answers or just guessing. They tested the model by having it try to solve the problem on its own, and then by having an expert check the model’s answers. Surprisingly, the model didn’t do very well in either case! It seems that even when the model tries to correct itself, it doesn’t actually get better at solving the problem. The results show that these models are not as good at reasoning as we thought. |
Keywords
» Artificial intelligence » Prompt » Prompting
