Summary of Questioning Internal Knowledge Structure Of Large Language Models Through the Lens Of the Olympic Games, by Juhwan Choi et al.
Questioning Internal Knowledge Structure of Large Language Models Through the Lens of the Olympic Games
by Juhwan Choi, YoungBin Kim
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large language models have excelled in natural language processing, but their internal workings remain largely unexplored. This paper delves into the internal knowledge structures of these models using Olympic Games medal tallies as a test case. The task is to provide medal counts for each team and identify specific rankings. Our findings reveal that state-of-the-art LLMs excel at reporting individual team medal counts, but struggle with questions about specific rankings, suggesting fundamentally different internal structures compared to human inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at understanding language, but scientists don’t really know how they work inside. Researchers looked at Olympic medal counts and asked the models to tell them which teams won medals and in what order they finished. The results show that these models are great at telling us who won what, but get confused when we ask about specific rankings. This is different from how humans think, where we can easily figure out rankings just by knowing who won what. |
Keywords
» Artificial intelligence » Inference » Natural language processing
