Summary of Are Llms Classical or Nonmonotonic Reasoners? Lessons From Generics, by Alina Leidinger et al.
Are LLMs classical or nonmonotonic reasoners? Lessons from generics
by Alina Leidinger, Robert van Rooij, Ekaterina Shutova
First submitted to arxiv on: 5 Jun 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 This paper investigates the nonmonotonic reasoning abilities of seven state-of-the-art large language models (LLMs) in abstract and commonsense reasoning tasks. The study reveals that while LLMs exhibit reasoning patterns similar to human nonmonotonic reasoning, they struggle to maintain stable beliefs on truth conditions of generics when presented with supporting examples or unrelated information. The findings highlight the limitations of attributing human reasoning behaviors to LLMs and assessing their general capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models think and reason. Researchers tested seven powerful AI models on a tricky task: understanding generic statements, like “Birds fly”, and exceptions, like “Penguins don’t fly”. The results show that these AI models can sometimes reason like humans, but they also make mistakes when new information is added. This helps us understand what AI is good at and where it still needs to improve. |
