Summary of Understanding Understanding: a Pragmatic Framework Motivated by Large Language Models, By Kevin Leyton-brown and Yoav Shoham
Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
by Kevin Leyton-Brown, Yoav Shoham
First submitted to arxiv on: 16 Jun 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 proposes a framework to determine whether an agent (human or machine) understands a subject matter. The approach is based on the agent’s performance in answering questions, similar to the Turing test. The framework includes defining the scope of understanding, requiring general competence, and allowing for incorrect answers. To achieve high confidence, the authors suggest using random sampling and probabilistic confidence bounds. They also show that providing explanations with answers can improve the sample complexity required to achieve acceptable bounds. According to the framework, current Large Language Models (LLMs) do not demonstrate understanding of nontrivial domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a way to test if machines or people understand something. The method checks how well they answer questions. It’s like a special kind of quiz. To make sure it works, the authors suggest using random questions and checking how often the answers are correct. They also found that giving explanations with answers helps make it easier to figure out what someone (or machine) really knows. |
