Summary of Easy Problems That Llms Get Wrong, by Sean Williams et al.
Easy Problems That LLMs Get Wrong
by Sean Williams, James Huckle
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper introduces a benchmark designed to evaluate the limitations of Large Language Models (LLMs) in various domains such as logical reasoning, spatial intelligence, and linguistic understanding. The authors use straightforward questions to show that well-regarded models struggle with tasks that humans perform easily, highlighting the potential of prompt engineering to mitigate errors. The study emphasizes the importance of grounding LLMs with human reasoning and common sense for enterprise applications. It also underscores the need for better training methodologies and human-in-the-loop approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how big language models are not as smart as they seem. It asks them simple questions that humans can answer easily, but the models struggle. This is important because we want these models to be useful in real-life situations, like helping businesses make decisions. The authors think that these models need to be taught more about human reasoning and common sense, rather than just relying on computers to figure things out. |
Keywords
* Artificial intelligence * Grounding * Prompt
