Summary of Using Gpt-4 to Guide Causal Machine Learning, by Anthony C. Constantinou et al.
Using GPT-4 to guide causal machine learning
by Anthony C. Constantinou, Neville K. Kitson, Alessio Zanga
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 paper investigates the ability of Large Language Models (LLMs) like GPT-4 (Turbo) to identify causal relationships. The authors focus on evaluating the performance of GPT-4 in isolating its ability to infer causal relationships based solely on variable labels without human context. They compare GPT-4’s performance with that of domain experts’ knowledge graphs and causal Machine Learning (ML) methods. The results show that participants judge GPT-4’s graphs as most accurate, followed by domain experts’ knowledge graphs, with causal ML lagging behind. The authors highlight the limitation of causal ML in producing inaccurate causal graphs and demonstrate how pairing GPT-4 with causal ML can overcome this limitation, resulting in more accurate graphical structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores whether a language model like ChatGPT can understand cause-and-effect relationships. They test GPT-4’s ability to figure out what causes what, just by looking at labels and not getting any help from humans. The results show that people think GPT-4 does a great job of creating these cause-and-effect diagrams, almost as good as experts who create their own diagrams. However, when the authors use special machine learning techniques to create these diagrams, they don’t do as well. This shows that even though ChatGPT isn’t specifically designed to understand cause-and-effect relationships, it can still be helpful in this area. |
Keywords
» Artificial intelligence » Gpt » Language model » Machine learning
